This ancilliary page presents diagnostics for species distribution models, done here.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

1. Presence/absence data

Aceroides aceroides latipes

## SDM for: aceroides_aceroides_latipes

Abiotic parameters

## McFadden's pseudo-R2 is: 0.22 
## Tjur's pseudo-R2 is: 0.2 
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.29 1158 -0.008887 0.9929
om 0.7006 0.5908 1.186 0.2357
gravel -32.64 4133 -0.007899 0.9937
silt -1.057 0.6364 -1.661 0.09664
clay -0.4924 1.504 -0.3275 0.7433
arsenic -0.5843 0.7725 -0.7564 0.4494
cadmium -0.1074 0.4716 -0.2278 0.8198
copper 0.7994 0.68 1.176 0.2398
iron -2.038 1.165 -1.75 0.08011
manganese 0.6651 0.727 0.9149 0.3602
mercury 0.5689 0.4664 1.22 0.2225 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.97 1 2.02 1.03 1.76 1.44 2.3 2.86 2.31 1.55

Influence indices

## McFadden's pseudo-R2 is: 0.36 
## Tjur's pseudo-R2 is: 0.35 
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.406 23.18 -0.1038 0.9173
aquaculture -10.12 4.854 -2.085 0.0371 *
city -5.22 4.227 -1.235 0.2169
dredging_collect 1.978 3.279 0.6032 0.5464
dredging_dump 8.674 4.346 1.996 0.04595 *
industry 4.368 2.179 2.004 0.04502 *
shipping_mooring -4.069 3.32 -1.226 0.2203
shipping_traffic 2.921 1.787 1.635 0.1021
sewers_rain 2.481 4.271 0.5808 0.5614
sewers_waste -6.139 5.702 -1.077 0.2816
wharves_city 6.9 5.089 1.356 0.1751
wharves_industry -17.11 7.422 -2.305 0.02117 *
fisheries_trap 0.2165 0.2923 0.7407 0.4589
fisheries_trawl -2.896 1.633 -1.774 0.07609
fisheries_net -1.098 240.2 -0.00457 0.9964
fisheries_dredge 3.109 1.821 1.708 0.08769 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 16.3 12.9 9.34 12.7 6.1 11 4.71 11.3 16.9 16.1 20.2 1.25 2.88 1 3.04

Akanthophoreus gracilis

## SDM for: akanthophoreus_gracilis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.22 
## Tjur's pseudo-R2 is: 0.22 
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.771 0.8599 -2.059 0.03946 *
om 0.7197 0.6251 1.151 0.2496
gravel -0.2559 0.3398 -0.7532 0.4513
silt -0.3166 0.5917 -0.535 0.5927
clay -2.078 4.404 -0.4718 0.6371
arsenic 1.78 0.8504 2.093 0.03639 *
cadmium -1.195 0.6626 -1.804 0.07118
copper -0.4515 0.8751 -0.5159 0.6059
iron -1.121 1.031 -1.088 0.2766
manganese -0.5799 0.9276 -0.6252 0.5319
mercury 0.669 0.5269 1.27 0.2041 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.13 1.22 2.16 1.12 2.32 1.94 2.81 2.54 2.94 1.75

Influence indices

## McFadden's pseudo-R2 is: 0.4 
## Tjur's pseudo-R2 is: 0.41 
## Pearson's pseudo-R2 is: 0.4
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -49.01 9593 -0.005108 0.9959
aquaculture 0.8755 3.741 0.234 0.815
city -1.661 11.6 -0.1432 0.8861
dredging_collect 1.108 2.764 0.401 0.6884
dredging_dump -3.884 3.925 -0.9894 0.3224
industry 3.212 1.799 1.786 0.07414
shipping_mooring 0.4912 4.994 0.09835 0.9217
shipping_traffic 2.458 1.558 1.578 0.1146
sewers_rain -5.053 4.636 -1.09 0.2758
sewers_waste 5.851 6.716 0.8712 0.3836
wharves_city 2.392 9.793 0.2442 0.8071
wharves_industry -2.126 4.916 -0.4325 0.6654
fisheries_trap -0.02259 0.5791 -0.03901 0.9689
fisheries_trawl 0.0143 0.3483 0.04105 0.9673
fisheries_net -484.5 99470 -0.004871 0.9961
fisheries_dredge 1.677 1.131 1.483 0.1381 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 12.4 24 8.31 11.3 4.32 17.4 4.31 14.3 23.1 20.6 14.1 1.14 1.52 1 2.06

Ameritella agilis

## SDM for: ameritella_agilis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.28 
## Tjur's pseudo-R2 is: 0.2 
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.757 1.603 -2.968 0.002999 * *
om -0.2253 0.9006 -0.2502 0.8024
gravel 0.3814 0.3982 0.9576 0.3383
silt 0.4714 0.7113 0.6627 0.5075
clay -0.0246 1.79 -0.01375 0.989
arsenic 1.118 1.092 1.024 0.3059
cadmium 1.559 0.8039 1.94 0.05242
copper -0.5074 0.9039 -0.5613 0.5746
iron 0.6741 0.7714 0.8739 0.3822
manganese -5.377 2.602 -2.067 0.03877 *
mercury -0.7168 1.106 -0.6481 0.517 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.86 1.22 1.68 1.05 1.99 2.09 2.38 1.77 2.47 1.49

Influence indices

## McFadden's pseudo-R2 is: 0.67 
## Tjur's pseudo-R2 is: 0.66 
## Pearson's pseudo-R2 is: 0.69
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.97 37.02 -0.3232 0.7465
aquaculture -4.204 22.96 -0.1831 0.8547
city -18.85 20.01 -0.9422 0.3461
dredging_collect 4.373 23.92 0.1828 0.855
dredging_dump 26.8 27.7 0.9675 0.3333
industry -4.854 14.32 -0.339 0.7346
shipping_mooring 1.527 9.388 0.1626 0.8708
shipping_traffic -9.804 5.459 -1.796 0.07252
sewers_rain 20.45 38.76 0.5276 0.5978
sewers_waste -17.64 63.79 -0.2765 0.7821
wharves_city 7.362 25.91 0.2841 0.7763
wharves_industry -28.11 34.59 -0.8127 0.4164
fisheries_trap 0.9609 0.7229 1.329 0.1838
fisheries_trawl 0.9924 1.225 0.81 0.4179
fisheries_net -1.18 378.9 -0.003114 0.9975
fisheries_dredge 1.981 4.632 0.4276 0.669 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 40.6 37.4 22.1 26 22.6 13.4 6.67 58.7 102 37.3 28.7 1.39 2.42 1 4.81

Ameroculodes edwardsi

## SDM for: ameroculodes_edwardsi

Abiotic parameters

## McFadden's pseudo-R2 is: 0.39 
## Tjur's pseudo-R2 is: 0.28 
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -314.2 81984 -0.003832 0.9969
om 1.651 1.331 1.24 0.2148
gravel 0.3069 0.4578 0.6704 0.5026
silt -0.3607 1.104 -0.3267 0.7439
clay -1687 447375 -0.003771 0.997
arsenic 0.6439 2.669 0.2413 0.8093
cadmium -0.7745 1.33 -0.5821 0.5605
copper 0.7823 1.34 0.5837 0.5594
iron -1.102 2.237 -0.4924 0.6224
manganese -4.297 3.914 -1.098 0.2722
mercury -0.3737 1.538 -0.243 0.808 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.24 1.31 2.14 1 1.99 2.14 2.2 2.8 2.27 1.71

Influence indices

## McFadden's pseudo-R2 is: -10.88 
## Tjur's pseudo-R2 is: 0.38 
## Pearson's pseudo-R2 is: 0.14
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.128e+15 7496963 -283847923 0 * * *
aquaculture 1.508e+15 66771996 22587849 0 * * *
city 4.043e+15 60515998 66814389 0 * * *
dredging_collect 1.335e+15 47886179 27883094 0 * * *
dredging_dump -2.001e+15 55589796 -3.6e+07 0 * * *
industry 2.308e+15 30193191 76456513 0 * * *
shipping_mooring 2.114e+14 51224704 4126278 0 * * *
shipping_traffic 1.552e+15 22815885 68021076 0 * * *
sewers_rain -2.312e+15 67164046 -34421159 0 * * *
sewers_waste 2.884e+15 90359677 31919241 0 * * *
wharves_city -2.771e+15 72465526 -38238294 0 * * *
wharves_industry -2.112e+15 79067645 -26705228 0 * * *
fisheries_trap -6.957e+14 7277539 -95590201 0 * * *
fisheries_trawl 2.102e+13 8821984 2382627 0 * * *
fisheries_net -7.662e+13 7219163 -10612931 0 * * *
fisheries_dredge 1.903e+14 19454028 9784359 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Ampelisca vadorum

## SDM for: ampelisca_vadorum

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -138.7 383647 -0.0003616 0.9997
om 23.82 743810 3.202e-05 1
gravel 16.8 155156 0.0001083 0.9999
silt -5.391 440444 -1.224e-05 1
clay -85.72 2750414 -3.117e-05 1
arsenic 28.19 392691 7.179e-05 0.9999
cadmium -50.24 2550096 -1.97e-05 1
copper -10.97 1315122 -8.341e-06 1
iron -9.734 111701 -8.715e-05 0.9999
manganese 24.98 2908626 8.587e-06 1
mercury -36.25 3440016 -1.054e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 10.2 6.67 8.91 4.86 6.33 27.5 15.2 1.88 24.2 33.1

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -45.89 81532 -0.0005628 0.9996
aquaculture 17.39 823645 2.111e-05 1
city -8.247 649404 -1.27e-05 1
dredging_collect 4.963 615293 8.066e-06 1
dredging_dump 1.678 624796 2.686e-06 1
industry -18.92 414071 -4.569e-05 1
shipping_mooring -8.321 665104 -1.251e-05 1
shipping_traffic 17.95 220308 8.15e-05 0.9999
sewers_rain -56 976368 -5.736e-05 1
sewers_waste 66.1 1193278 5.54e-05 1
wharves_city 9.151 874298 1.047e-05 1
wharves_industry -6.093 588168 -1.036e-05 1
fisheries_trap -36.56 400329 -9.133e-05 0.9999
fisheries_trawl 1.048 71933 1.458e-05 1
fisheries_net 3.805 96171 3.956e-05 1
fisheries_dredge -2.15 180119 -1.194e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 16.4 20.7 20.4 21.5 11.5 17.7 4.35 15.2 22.8 28.1 18.4 3.25 3.22 1.7 2.27

Amphipoda

## SDM for: amphipoda

Abiotic parameters

## McFadden's pseudo-R2 is: 0.06 
## Tjur's pseudo-R2 is: 0.07 
## Pearson's pseudo-R2 is: 0.07
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6341 0.2661 -2.383 0.01718 *
om 0.2529 0.4543 0.5566 0.5778
gravel 0.141 0.2795 0.5047 0.6138
silt -0.07723 0.4908 -0.1574 0.875
clay 0.2773 0.668 0.4152 0.678
arsenic 0.4102 0.4092 1.002 0.3162
cadmium 0.1656 0.3405 0.4864 0.6267
copper -0.1501 0.5391 -0.2784 0.7807
iron -0.5428 0.7224 -0.7515 0.4524
manganese 0.176 0.4838 0.3639 0.716
mercury 0.2019 0.3908 0.5165 0.6055 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.98 1.31 2.14 1.1 1.61 1.4 2.35 2.34 1.99 1.55

Influence indices

## McFadden's pseudo-R2 is: 0.24 
## Tjur's pseudo-R2 is: 0.28 
## Pearson's pseudo-R2 is: 0.28
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -47.03 6419 -0.007327 0.9942
aquaculture -4.229 2.448 -1.728 0.08405
city 2.732 2.985 0.9153 0.3601
dredging_collect 0.3684 1.909 0.193 0.847
dredging_dump 5.328 2.684 1.985 0.04715 *
industry 3.257 1.217 2.675 0.007467 * *
shipping_mooring -2.543 2.066 -1.231 0.2183
shipping_traffic -0.2417 0.992 -0.2437 0.8075
sewers_rain 3.178 3.076 1.033 0.3016
sewers_waste -3.541 3.899 -0.9083 0.3637
wharves_city -2.933 3.271 -0.8967 0.3699
wharves_industry -7.405 4.007 -1.848 0.06461
fisheries_trap 0.4169 0.254 1.641 0.1007
fisheries_trawl 0.1173 0.2941 0.3989 0.69
fisheries_net -478.1 66555 -0.007183 0.9943
fisheries_dredge 0.3911 0.694 0.5636 0.573 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.2 10.9 6.61 9.28 4.74 7.87 3.63 11.4 15.5 11.1 13.9 1.22 1.38 1 1.76

Anonyx lilljeborgi

## SDM for: anonyx_lilljeborgi

Abiotic parameters

## McFadden's pseudo-R2 is: 0.12 
## Tjur's pseudo-R2 is: 0.1 
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.589 0.581 -4.455 8.373e-06 * * *
om 0.06397 0.7658 0.08353 0.9334
gravel -0.2129 0.4163 -0.5113 0.6091
silt -0.3808 0.6665 -0.5714 0.5677
clay -0.05949 1.455 -0.04089 0.9674
arsenic -0.5382 1.396 -0.3854 0.7
cadmium -0.4654 0.5505 -0.8455 0.3978
copper -0.3638 0.9078 -0.4007 0.6886
iron -0.9676 1.131 -0.8553 0.3924
manganese 1.03 1.088 0.9469 0.3437
mercury 0.005688 0.7112 0.007998 0.9936 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.67 1.2 1.76 1.07 1.84 1.36 2.04 2.1 2.48 1.6

Influence indices

## McFadden's pseudo-R2 is: 0.36 
## Tjur's pseudo-R2 is: 0.32 
## Pearson's pseudo-R2 is: 0.33
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -47.62 9701 -0.004908 0.9961
aquaculture -6.816 7.895 -0.8633 0.388
city -16.81 16.99 -0.9892 0.3226
dredging_collect -4.376 4.464 -0.9801 0.327
dredging_dump 3.381 4.36 0.7753 0.4381
industry 0.728 2.93 0.2485 0.8038
shipping_mooring -3.534 5.65 -0.6254 0.5317
shipping_traffic 2.857 2.531 1.129 0.2589
sewers_rain 0.7968 4.61 0.1728 0.8628
sewers_waste -5.441 8.423 -0.646 0.5183
wharves_city 17.11 17.04 1.004 0.3152
wharves_industry -3.693 6.282 -0.5879 0.5566
fisheries_trap -0.1464 0.5325 -0.275 0.7833
fisheries_trawl -9.673 8.778 -1.102 0.2705
fisheries_net -436.7 100591 -0.004341 0.9965
fisheries_dredge -1.991 1.905 -1.045 0.2959 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 20.6 38.6 8.51 9.15 4.25 14.3 5.88 10.6 18.8 41.7 12 1.13 1.6 1 2.38

Anthozoa

## SDM for: anthozoa

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Arcteobia anticostiensis

## SDM for: arcteobia_anticostiensis

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -157.4 448856 -0.0003506 0.9997
om -9.446 244648 -3.861e-05 1
gravel 1.884 549794 3.427e-06 1
silt 25.73 120585 0.0002134 0.9998
clay 3.612 2041819 1.769e-06 1
arsenic -99.38 527677 -0.0001883 0.9998
cadmium 46.64 389449 0.0001198 0.9999
copper 7.77 320737 2.423e-05 1
iron 5.156 458233 1.125e-05 1
manganese -96.32 760707 -0.0001266 0.9999
mercury -33.49 301617 -0.000111 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.49 2.61 2.07 1.42 1.71 5.8 3.83 4.91 6.15 2.97

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -132 261580 -0.0005048 0.9996
aquaculture 29.9 2012429 1.486e-05 1
city -44.81 2524621 -1.775e-05 1
dredging_collect -159.7 1088442 -0.0001467 0.9999
dredging_dump -24.51 2547376 -9.621e-06 1
industry -32.52 1014058 -3.207e-05 1
shipping_mooring -1.868 1479903 -1.262e-06 1
shipping_traffic -16.82 413635 -4.066e-05 1
sewers_rain -9.975 1818813 -5.484e-06 1
sewers_waste 47.74 3083842 1.548e-05 1
wharves_city 64.8 3296940 1.965e-05 1
wharves_industry 194.3 1348004 0.0001441 0.9999
fisheries_trap -166.4 673787 -0.0002469 0.9998
fisheries_trawl -108.3 748721 -0.0001446 0.9999
fisheries_net 5.202 183416 2.836e-05 1
fisheries_dredge 51.23 177084 0.0002893 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 30.6 29.4 14.3 34.2 9.02 22.4 6.04 31.7 53.2 36.3 18.3 2.25 2.44 1.2 2.14

Arrhoges occidentalis

## SDM for: arrhoges_occidentalis

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -697.8 1241825 -0.0005619 0.9996
om -307.8 642564 -0.0004791 0.9996
gravel -46.14 509443 -9.057e-05 0.9999
silt 215.6 523288 0.000412 0.9997
clay -835.8 3182082 -0.0002626 0.9998
arsenic -55.85 994346 -5.617e-05 1
cadmium -13.95 268185 -5.203e-05 1
copper 63.83 458454 0.0001392 0.9999
iron -218.9 718355 -0.0003047 0.9998
manganese 130 862172 0.0001508 0.9999
mercury -343.6 763174 -0.0004503 0.9996 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.49 1.68 10.5 2.47 5.16 5.64 4.88 4.87 5.22 7.25

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -60.14 143179 -0.00042 0.9997
aquaculture 107.6 1441852 7.459e-05 0.9999
city -1.428 1895135 -7.536e-07 1
dredging_collect 67.81 1364124 4.971e-05 1
dredging_dump 40.36 2054967 1.964e-05 1
industry -18.71 679982 -2.752e-05 1
shipping_mooring 25.27 1492206 1.693e-05 1
shipping_traffic 14.55 518221 2.807e-05 1
sewers_rain -152.9 2079740 -7.351e-05 0.9999
sewers_waste 219.1 2685278 8.161e-05 0.9999
wharves_city -6.408 2320513 -2.761e-06 1
wharves_industry -124.7 3709266 -3.361e-05 1
fisheries_trap 1.818 81054 2.243e-05 1
fisheries_trawl 6.144 131410 4.676e-05 1
fisheries_net 11.53 144282 7.992e-05 0.9999
fisheries_dredge -18.85 344009 -5.479e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 26.5 26.7 22.6 34.8 8.97 28.8 9.9 41.8 55.2 32.6 59.7 1.53 1.91 1.55 1.89

Astarte sp

## SDM for: astarte_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.49 
## Tjur's pseudo-R2 is: 0.35 
## Pearson's pseudo-R2 is: 0.32
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -363.5 106846 -0.003402 0.9973
om -1.474 2.398 -0.6148 0.5387
gravel -1.021 1.167 -0.8752 0.3814
silt -1.461 1.724 -0.8473 0.3968
clay -1941 583040 -0.003328 0.9973
arsenic 2.285 1.967 1.162 0.2453
cadmium -2.472 1.975 -1.252 0.2106
copper 3.352 2.021 1.659 0.09713
iron -0.1492 1.825 -0.08178 0.9348
manganese -7.684 6 -1.281 0.2003
mercury 0.8402 2.708 0.3103 0.7563 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.8 1.2 1.84 1 1.95 2.1 2.22 2.03 3.05 2.21

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -334.9 961323 -0.0003483 0.9997
aquaculture -773.8 6568153 -0.0001178 0.9999
city 1307 6289563 0.0002078 0.9998
dredging_collect 817.9 4639745 0.0001763 0.9999
dredging_dump 437 6511840 6.71e-05 0.9999
industry 322.4 1962421 0.0001643 0.9999
shipping_mooring 19.7 7149191 2.756e-06 1
shipping_traffic 400.1 4353995 9.189e-05 0.9999
sewers_rain 848.7 6023976 0.0001409 0.9999
sewers_waste -1559 7554110 -0.0002064 0.9998
wharves_city -1404 6946723 -0.0002022 0.9998
wharves_industry -1399 4168846 -0.0003356 0.9997
fisheries_trap -80.57 851379 -9.463e-05 0.9999
fisheries_trawl 3.774 2717836 1.389e-06 1
fisheries_net 25.73 6458767 3.984e-06 1
fisheries_dredge 79.87 751952 0.0001062 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 66.6 79.1 54.5 93.7 26.5 98.1 79 107 99.9 89.9 60.1 3.98 9.78 1 16.9

Axinopsida orbiculata

## SDM for: axinopsida_orbiculata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.15 
## Tjur's pseudo-R2 is: 0.13 
## Pearson's pseudo-R2 is: 0.15
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -350.2 50091 -0.006991 0.9944
om 0.179 0.7083 0.2527 0.8005
gravel 0.1448 0.3706 0.3909 0.6959
silt 0.4586 0.7545 0.6079 0.5433
clay -1899 273336 -0.006949 0.9945
arsenic 0.1748 0.4868 0.3591 0.7195
cadmium 0.4905 0.4235 1.158 0.2467
copper -0.04828 0.5662 -0.08528 0.932
iron 0.02784 0.3768 0.0739 0.9411
manganese -0.5204 0.802 -0.6489 0.5164
mercury -1.75 0.8706 -2.01 0.04448 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.03 1.23 2.22 1 1.46 1.49 1.73 1.4 1.67 1.65

Influence indices

## McFadden's pseudo-R2 is: 0.39 
## Tjur's pseudo-R2 is: 0.39 
## Pearson's pseudo-R2 is: 0.4
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.35 22.24 -0.1956 0.845
aquaculture -9.769 5.583 -1.75 0.08018
city 2.463 6.809 0.3618 0.7175
dredging_collect -8.538 4.209 -2.028 0.04252 *
dredging_dump 5.18 4.323 1.198 0.2308
industry 4.782 2.272 2.105 0.03532 *
shipping_mooring -11.07 5.824 -1.9 0.05737
shipping_traffic -0.1789 1.745 -0.1025 0.9183
sewers_rain -8.51 5.806 -1.466 0.1427
sewers_waste 5.623 7.137 0.7878 0.4308
wharves_city 3.271 8.576 0.3814 0.7029
wharves_industry 2.262 6.356 0.356 0.7219
fisheries_trap 0.2228 0.4098 0.5437 0.5866
fisheries_trawl 1.244 0.6119 2.033 0.0421 *
fisheries_net 0.1132 230.4 0.0004914 0.9996
fisheries_dredge -0.6707 1.376 -0.4873 0.6261 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 16.1 18.7 11.9 12 6.45 14.9 4.58 11.8 16.8 24.4 17.3 1.78 1.77 1 1.99

Axiothella catenata

## SDM for: axiothella_catenata

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -124.5 239182 -0.0005206 0.9996
om 48.35 223221 0.0002166 0.9998
gravel 4.668 104016 4.488e-05 1
silt 15.06 299118 5.035e-05 1
clay 29.53 414785 7.12e-05 0.9999
arsenic -98.17 2115404 -4.641e-05 1
cadmium 6.723 548780 1.225e-05 1
copper 30.86 658438 4.687e-05 1
iron -96.12 1648993 -5.829e-05 1
manganese 19.64 974923 2.015e-05 1
mercury 13.78 199905 6.895e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.35 1.16 3.13 1.12 11.8 5.99 5.84 14.1 7.66 3.88

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -172.6 710906 -0.0002428 0.9998
aquaculture 274.2 12346720 2.221e-05 1
city 438 10605133 4.13e-05 1
dredging_collect -41.52 7330176 -5.665e-06 1
dredging_dump 402.9 9883995 4.077e-05 1
industry -113 2977563 -3.795e-05 1
shipping_mooring 266.6 2888043 9.232e-05 0.9999
shipping_traffic 144 2229645 6.46e-05 0.9999
sewers_rain -202.5 24235051 -8.357e-06 1
sewers_waste 127.5 31882221 3.999e-06 1
wharves_city -519.7 9085034 -5.721e-05 1
wharves_industry -335.1 7301761 -4.59e-05 1
fisheries_trap 2.331 165526 1.408e-05 1
fisheries_trawl 21.04 1544162 1.363e-05 1
fisheries_net 43.25 1455681 2.971e-05 1
fisheries_dredge 99.04 4693485 2.11e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 139 111 87.7 111 20 33.2 23.7 225 345 93.9 79.1 1.26 18.4 5.76 49.2

Bathymedon longimanus

## SDM for: bathymedon_longimanus

Abiotic parameters

## McFadden's pseudo-R2 is: 0.75 
## Tjur's pseudo-R2 is: 0.68 
## Pearson's pseudo-R2 is: 0.67
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -45.76 6751 -0.006779 0.9946
om -0.9562 5.027 -0.1902 0.8491
gravel -16.64 6173 -0.002695 0.9978
silt 3.376 5.74 0.5882 0.5564
clay -66.12 35621 -0.001856 0.9985
arsenic -19.88 22.7 -0.8757 0.3812
cadmium 7.025 5.106 1.376 0.1689
copper 2.347 4.458 0.5264 0.5986
iron 0.8915 9.592 0.09293 0.926
manganese -18.37 18.36 -1.001 0.3169
mercury -11.6 8.551 -1.356 0.175 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.63 1 5.49 1 3.42 3.38 2.35 4.45 3.85 2.99

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -150.9 279889 -0.0005392 0.9996
aquaculture 546.3 2309983 0.0002365 0.9998
city 252.9 2641627 9.573e-05 0.9999
dredging_collect -76.37 1312369 -5.819e-05 1
dredging_dump -39.15 1310039 -2.989e-05 1
industry -145.9 1131859 -0.0001289 0.9999
shipping_mooring 182.8 2323752 7.866e-05 0.9999
shipping_traffic 20.47 555277 3.686e-05 1
sewers_rain -580.1 2351575 -0.0002467 0.9998
sewers_waste 800.3 3192021 0.0002507 0.9998
wharves_city -236.6 3057641 -7.739e-05 0.9999
wharves_industry 205.5 2118130 9.701e-05 0.9999
fisheries_trap -15.54 108138 -0.0001437 0.9999
fisheries_trawl 1.348 838528 1.608e-06 1
fisheries_net 42.88 240506 0.0001783 0.9999
fisheries_dredge 52.99 170692 0.0003104 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 50.2 80.2 37.2 39 15.6 59.3 16.4 57.8 78.2 95.5 58 1.28 3.11 1.57 3.36

Bathymedon obtusifrons

## SDM for: bathymedon_obtusifrons

Abiotic parameters

## McFadden's pseudo-R2 is: -12.23 
## Tjur's pseudo-R2 is: -0.01 
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.301e+15 7978909 -413766906 0 * * *
om -1.493e+15 14257309 -104685824 0 * * *
gravel 7.237e+14 8714549 83047230 0 * * *
silt 1.384e+15 15274819 90612828 0 * * *
clay -7.527e+14 21858102 -34434528 0 * * *
arsenic -1.752e+15 11951982 -146562517 0 * * *
cadmium 6.267e+14 10400421 60260861 0 * * *
copper 1.939e+15 13687347 141670271 0 * * *
iron -4.869e+14 9620115 -50615564 0 * * *
manganese -5.417e+14 14288707 -37912203 0 * * *
mercury -6.123e+14 12068606 -50738739 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -108.2 230852 -0.0004686 0.9996
aquaculture -372.7 7074900 -5.267e-05 1
city -426.4 3308637 -0.0001289 0.9999
dredging_collect -235.4 21696254 -1.085e-05 1
dredging_dump -82.52 6321537 -1.305e-05 1
industry 213.3 1182221 0.0001805 0.9999
shipping_mooring -280.9 11462006 -2.451e-05 1
shipping_traffic -98.85 3244147 -3.047e-05 1
sewers_rain 103.2 6422833 1.607e-05 1
sewers_waste -34.09 8749498 -3.896e-06 1
wharves_city 620.2 4779428 0.0001298 0.9999
wharves_industry 91.44 28604720 3.197e-06 1
fisheries_trap -14.2 464038 -3.06e-05 1
fisheries_trawl 11.79 560768 2.102e-05 1
fisheries_net 5.979 272327 2.195e-05 1
fisheries_dredge 40.26 6081530 6.621e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 58.6 47.9 255 80.8 13 103 34.1 46.3 61.4 78.8 312 9.76 3.81 1.77 36.1

Bipalponephtys neotena

## SDM for: bipalponephtys_neotena

Abiotic parameters

## McFadden's pseudo-R2 is: 0.34 
## Tjur's pseudo-R2 is: 0.35 
## Pearson's pseudo-R2 is: 0.36
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.725 0.7255 3.757 0.0001721 * * *
om 3.351 1.061 3.158 0.001587 * *
gravel 0.1487 0.3701 0.4019 0.6878
silt 0.07557 0.5909 0.1279 0.8982
clay 0.1914 0.7378 0.2595 0.7953
arsenic -0.5925 0.6365 -0.931 0.3519
cadmium 0.2332 0.5077 0.4593 0.646
copper -0.6341 0.663 -0.9564 0.3388
iron -0.4925 0.3589 -1.372 0.17
manganese 0.4055 0.9583 0.4231 0.6722
mercury -0.187 0.8137 -0.2298 0.8182 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.62 1.21 1.63 1.13 1.56 1.62 1.82 1.47 1.89 1.56

Influence indices

## McFadden's pseudo-R2 is: 0.28 
## Tjur's pseudo-R2 is: 0.28 
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 75.64 8314 0.009098 0.9927
aquaculture -2.509 3.254 -0.7713 0.4405
city -1.976 2.874 -0.6875 0.4918
dredging_collect 0.6187 2.508 0.2466 0.8052
dredging_dump 2.541 2.718 0.9349 0.3498
industry 3.105 1.637 1.897 0.05787
shipping_mooring -0.9498 2.381 -0.3989 0.69
shipping_traffic 0.1875 1.08 0.1737 0.8621
sewers_rain 0.01476 3.206 0.004605 0.9963
sewers_waste 0.9926 4.578 0.2168 0.8283
wharves_city 2.743 3.61 0.7599 0.4473
wharves_industry -6.209 3.43 -1.81 0.07025
fisheries_trap 0.5818 0.7865 0.7397 0.4595
fisheries_trawl -0.116 0.3149 -0.3683 0.7127
fisheries_net 763.1 86210 0.008851 0.9929
fisheries_dredge 1.454 1.483 0.98 0.3271 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.9 10.4 8.85 9.53 5.97 6.98 3.9 9.91 13.9 12.7 12.1 1.1 1.46 1 2.15

Boreochiton ruber

## SDM for: boreochiton_ruber

Abiotic parameters

## McFadden's pseudo-R2 is: 0.61 
## Tjur's pseudo-R2 is: 0.51 
## Pearson's pseudo-R2 is: 0.54
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -416.3 128343 -0.003244 0.9974
om -14.19 12.19 -1.164 0.2446
gravel 0.61 1.068 0.5713 0.5678
silt 3.379 3.183 1.062 0.2883
clay -2147 700348 -0.003065 0.9976
arsenic -9.387 11.1 -0.8457 0.3977
cadmium 1.35 2.559 0.5273 0.598
copper 4.62 5.155 0.8962 0.3702
iron -1.693 7.135 -0.2372 0.8125
manganese -4.797 8.229 -0.5829 0.56
mercury -5.783 5.647 -1.024 0.3058 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.44 2.23 3.53 1 2.04 1.69 3.61 3.89 2.79 3.31

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -419.6 1299699 -0.0003229 0.9997
aquaculture -393.4 6744588 -5.832e-05 1
city 1195 3482669 0.0003432 0.9997
dredging_collect 156.6 3564214 4.394e-05 1
dredging_dump 444 2609046 0.0001702 0.9999
industry -242 2960165 -8.175e-05 0.9999
shipping_mooring 68.55 4602557 1.489e-05 1
shipping_traffic 20.41 1266934 1.611e-05 1
sewers_rain 1158 3858791 3e-04 0.9998
sewers_waste -2138 6717074 -0.0003183 0.9997
wharves_city -1550 5275231 -0.0002938 0.9998
wharves_industry 41.58 3430100 1.212e-05 1
fisheries_trap -10.43 99035 -0.0001053 0.9999
fisheries_trawl 14.27 95952 0.0001487 0.9999
fisheries_net 7.98 6464702 1.234e-06 1
fisheries_dredge 15.65 276619 5.658e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 226 90.7 66.4 51.5 111 75.2 35.8 107 203 74 71.4 1.36 2.65 1.07 5.19

Brachydiastylis sp

## SDM for: brachydiastylis_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.56 
## Tjur's pseudo-R2 is: 0.43 
## Pearson's pseudo-R2 is: 0.45
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -259.1 123682 -0.002095 0.9983
om -4.988 5.363 -0.9301 0.3523
gravel 1.193 0.9731 1.226 0.2201
silt 3.894 4.309 0.9038 0.3661
clay -1353 674913 -0.002005 0.9984
arsenic 1.927 3.475 0.5546 0.5792
cadmium -1.356 2.631 -0.5151 0.6065
copper -1.304 4.147 -0.3145 0.7532
iron -0.2421 1.607 -0.1506 0.8803
manganese 2.174 5.36 0.4057 0.685
mercury -6.953 7.487 -0.9286 0.3531 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.93 1.89 3.95 1 1.56 2.17 1.89 1.31 2.33 2.63

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -44.02 78423 -0.0005613 0.9996
aquaculture -2.298 1078585 -2.131e-06 1
city 9.953 892781 1.115e-05 1
dredging_collect 15.66 627088 2.498e-05 1
dredging_dump 14.01 531114 2.637e-05 1
industry -13.15 455368 -2.887e-05 1
shipping_mooring -3.474 818722 -4.243e-06 1
shipping_traffic 25.57 225177 0.0001135 0.9999
sewers_rain -29.05 1993066 -1.457e-05 1
sewers_waste 26.45 2582489 1.024e-05 1
wharves_city -8.742 1186999 -7.365e-06 1
wharves_industry -34.78 636776 -5.461e-05 1
fisheries_trap -35.95 481385 -7.467e-05 0.9999
fisheries_trawl 2.486 67342 3.692e-05 1
fisheries_net 3.375 138615 2.435e-05 1
fisheries_dredge 17.78 214484 8.29e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 21.9 27.4 21.2 17.9 13 21.5 4.61 34 50.8 36.2 19.3 4.08 3.31 2.45 4.02

Byblis gaimardii

## SDM for: byblis_gaimardii

Abiotic parameters

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.16 
## Pearson's pseudo-R2 is: 0.17
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -314.3 80459 -0.003907 0.9969
om 0.06122 1.704 0.03594 0.9713
gravel 0.3874 0.4455 0.8696 0.3845
silt -0.1092 1.346 -0.08116 0.9353
clay -1687 439052 -0.003843 0.9969
arsenic 0.9056 2.505 0.3616 0.7177
cadmium -0.6533 1.66 -0.3935 0.694
copper -1.419 2.128 -0.6668 0.5049
iron -0.5601 1.729 -0.324 0.746
manganese -0.4225 4.268 -0.09899 0.9212
mercury -0.4148 2.158 -0.1922 0.8476 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.61 1.26 2.06 1 1.47 1.59 1.47 1.47 1.89 1.79

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -133.5 633415 -0.0002108 0.9998
aquaculture -33.81 3011137 -1.123e-05 1
city -55.38 5123744 -1.081e-05 1
dredging_collect -42.89 6933315 -6.186e-06 1
dredging_dump -8.803 3069272 -2.868e-06 1
industry -27.66 2327512 -1.188e-05 1
shipping_mooring -60.48 3446335 -1.755e-05 1
shipping_traffic 65.97 849523 7.765e-05 0.9999
sewers_rain -10.59 2937710 -3.604e-06 1
sewers_waste 40.72 6562802 6.205e-06 1
wharves_city 75.86 7142573 1.062e-05 1
wharves_industry 4.933 9122226 5.408e-07 1
fisheries_trap -284.1 899705 -0.0003158 0.9997
fisheries_trawl -1.333 136060 -9.8e-06 1
fisheries_net 4.65 260389 1.786e-05 1
fisheries_dredge 60.26 974431 6.184e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 34.2 45.8 84.9 36.4 17.1 42.4 10.3 41.3 89.8 63.7 110 1.49 3.11 1.04 8.79

Cancer irroratus

## SDM for: cancer_irroratus

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -511.8 862740 -0.0005932 0.9995
om -261.7 454872 -0.0005754 0.9995
gravel 86.71 157831 0.0005494 0.9996
silt 75.79 203871 0.0003718 0.9997
clay 43.83 271758 0.0001613 0.9999
arsenic 54.42 2718342 2.002e-05 1
cadmium 229.9 761955 0.0003017 0.9998
copper 104.5 342807 0.0003048 0.9998
iron -256.6 4070557 -6.304e-05 0.9999
manganese -191.8 3350724 -5.723e-05 1
mercury -14.6 1024508 -1.425e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.79 3.93 3.6 1.1 13.2 11 4.14 31.3 26.9 6.64

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -239.4 988003 -0.0002423 0.9998
aquaculture 1113 17795634 6.255e-05 1
city 385.5 2236045 0.0001724 0.9999
dredging_collect 133.7 12374406 1.08e-05 1
dredging_dump -81.54 3307523 -2.465e-05 1
industry -464.9 2014431 -0.0002308 0.9998
shipping_mooring 701.1 3892682 0.0001801 0.9999
shipping_traffic 186.5 2400153 7.772e-05 0.9999
sewers_rain -409.1 16316413 -2.507e-05 1
sewers_waste 636.8 21629851 2.944e-05 1
wharves_city -596.9 3843564 -0.0001553 0.9999
wharves_industry 15.41 12632497 1.22e-06 1
fisheries_trap 32.12 262169 0.0001225 0.9999
fisheries_trawl -42.42 1651366 -2.569e-05 1
fisheries_net 4.778 578096 8.264e-06 1
fisheries_dredge -125.8 15207593 -8.275e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 231 55.9 212 39.3 35.2 45.5 49.4 181 185 69.6 223 7.63 9.47 2.29 79.5

Caprella septentrionalis

## SDM for: caprella_septentrionalis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.63 
## Tjur's pseudo-R2 is: 0.53 
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -15.34 7.501 -2.045 0.0409 *
om -4.194 2.9 -1.446 0.1481
gravel 2.214 1.629 1.359 0.1741
silt -2.769 2.096 -1.321 0.1863
clay 3.987 2.45 1.627 0.1037
arsenic -6.901 4.837 -1.427 0.1536
cadmium 7.771 4.227 1.839 0.06597
copper 11.11 5.789 1.918 0.05509
iron -15.88 8.848 -1.795 0.07261
manganese 0.05921 2.965 0.01997 0.9841
mercury 1.467 1.957 0.7496 0.4535 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.73 2.6 2.47 2.1 2.19 5.05 8.16 7.06 2.19 1.57

Influence indices

## McFadden's pseudo-R2 is: -7.28 
## Tjur's pseudo-R2 is: 0.47 
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.078e+15 7496963 -277184763 0 * * *
aquaculture 3.252e+13 66771996 487077 0 * * *
city -4.966e+12 60515998 -82064 0 * * *
dredging_collect -8.073e+13 47886179 -1685846 0 * * *
dredging_dump 2.529e+15 55589796 45499743 0 * * *
industry -4.734e+14 30193191 -15679140 0 * * *
shipping_mooring 1.659e+13 51224704 323856 0 * * *
shipping_traffic -6.103e+14 22815885 -26747331 0 * * *
sewers_rain 2.196e+15 67164046 32693463 0 * * *
sewers_waste -1.971e+15 90359677 -21810783 0 * * *
wharves_city -2.639e+14 72465526 -3642097 0 * * *
wharves_industry -2.066e+15 79067645 -26124635 0 * * *
fisheries_trap -3.869e+14 7277539 -53166922 0 * * *
fisheries_trawl -4.466e+14 8821984 -50626003 0 * * *
fisheries_net 9.012e+13 7219163 12483989 0 * * *
fisheries_dredge -2.551e+14 19454028 -13112042 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Chaetodermatida

## SDM for: chaetodermatida

Abiotic parameters

## McFadden's pseudo-R2 is: 0.15 
## Tjur's pseudo-R2 is: 0.11 
## Pearson's pseudo-R2 is: 0.1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.613 2.483 -1.455 0.1457
om -0.0259 0.7373 -0.03512 0.972
gravel -0.5285 0.5882 -0.8986 0.3689
silt -0.1829 0.6298 -0.2904 0.7715
clay -7.934 13.45 -0.5898 0.5553
arsenic 0.1186 0.8455 0.1403 0.8884
cadmium -0.6027 0.6787 -0.8879 0.3746
copper 0.4939 0.7447 0.6632 0.5072
iron -0.4932 0.7072 -0.6974 0.4855
manganese -0.9569 1.209 -0.7918 0.4285
mercury 0.08703 0.6661 0.1306 0.8961 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.67 1.11 1.71 1.02 1.55 1.63 1.9 1.63 1.99 1.5

Influence indices

## McFadden's pseudo-R2 is: 0.24 
## Tjur's pseudo-R2 is: 0.2 
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -66.5 6711 -0.009909 0.9921
aquaculture 4.806 5.27 0.912 0.3618
city 0.2363 4.736 0.0499 0.9602
dredging_collect 4.11 2.802 1.467 0.1425
dredging_dump -2.921 4.22 -0.6922 0.4888
industry 0.8149 1.867 0.4365 0.6625
shipping_mooring 2.377 3.562 0.6675 0.5045
shipping_traffic 1.524 1.497 1.018 0.3088
sewers_rain -0.7247 4.071 -0.178 0.8587
sewers_waste 3.27 7.203 0.454 0.6499
wharves_city 0.7321 6.646 0.1101 0.9123
wharves_industry -5.104 4.52 -1.129 0.2589
fisheries_trap -0.456 0.7794 -0.5851 0.5585
fisheries_trawl -0.09704 0.2995 -0.3241 0.7459
fisheries_net -660.9 69587 -0.009498 0.9924
fisheries_dredge 0.07327 0.7891 0.09286 0.926 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 12.4 12.8 7.44 10.6 4.47 9.58 4.05 11.7 18.8 17.2 11.2 1.07 1.38 1 1.74

Chionoecetes opilio

## SDM for: chionoecetes_opilio

Abiotic parameters

## McFadden's pseudo-R2 is: -6.59 
## Tjur's pseudo-R2 is: 0.49 
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.535e+15 7978909 -317727778 0 * * *
om 9.975e+13 14257309 6996528 0 * * *
gravel 8.095e+13 8714549 9288951 0 * * *
silt 1.36e+15 15274819 89028485 0 * * *
clay -2.073e+15 21858102 -94855449 0 * * *
arsenic -4.63e+14 11951982 -38739076 0 * * *
cadmium -3.6e+14 10400421 -34610298 0 * * *
copper -9.333e+13 13687347 -6818572 0 * * *
iron -3.853e+14 9620115 -40047527 0 * * *
manganese 8.513e+14 14288707 59580120 0 * * *
mercury -1.171e+15 12068606 -97010557 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -260.9 840224 -0.0003106 0.9998
aquaculture -88.12 24921514 -3.536e-06 1
city 198.3 17367840 1.142e-05 1
dredging_collect 372.2 9125675 4.078e-05 1
dredging_dump 1092 20887879 5.23e-05 1
industry 271.9 1597109 0.0001703 0.9999
shipping_mooring 233 30038188 7.756e-06 1
shipping_traffic -67.35 1616975 -4.165e-05 1
sewers_rain 779.6 9563099 8.152e-05 0.9999
sewers_waste -959.8 12619665 -7.605e-05 0.9999
wharves_city -443.6 19098772 -2.323e-05 1
wharves_industry -1623 5167607 -0.0003141 0.9997
fisheries_trap 32.51 1417017 2.295e-05 1
fisheries_trawl -217.9 2782677 -7.83e-05 0.9999
fisheries_net 11.9 392777 3.029e-05 1
fisheries_dredge -117.4 3157382 -3.718e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 385 222 134 324 19.6 491 31.1 184 226 246 78.7 22.8 9.58 1.56 25.1

Chlamys islandica

## SDM for: chlamys_islandica

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -182.6 473133 -0.0003859 0.9997
om -134.2 358347 -0.0003744 0.9997
gravel -4.482 286106 -1.566e-05 1
silt 64.12 285563 0.0002245 0.9998
clay -105.7 1398946 -7.555e-05 0.9999
arsenic 5.142 431968 1.19e-05 1
cadmium -40.83 219759 -0.0001858 0.9999
copper 62.63 258197 0.0002426 0.9998
iron 9.686 117058 8.274e-05 0.9999
manganese -19.25 328394 -5.861e-05 1
mercury -69.44 301137 -0.0002306 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.88 1.28 4.22 1.51 3.79 3.13 4.32 1.92 4.01 3.3

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -35.16 72323 -0.0004862 0.9996
aquaculture -24 1358385 -1.767e-05 1
city -12.59 1297078 -9.708e-06 1
dredging_collect -14.92 851895 -1.751e-05 1
dredging_dump -6.445 662645 -9.726e-06 1
industry 11.9 525131 2.266e-05 1
shipping_mooring -27.69 1438980 -1.924e-05 1
shipping_traffic -18.11 338086 -5.356e-05 1
sewers_rain -19.75 1022136 -1.932e-05 1
sewers_waste 24.54 1349796 1.818e-05 1
wharves_city 28.32 1807498 1.567e-05 1
wharves_industry 31.49 777801 4.048e-05 1
fisheries_trap -0.6731 83030 -8.106e-06 1
fisheries_trawl 10.88 67868 0.0001604 0.9999
fisheries_net 3.765 70648 5.329e-05 1
fisheries_dredge 0.6407 190947 3.355e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 27.5 18.2 14.2 11 9.45 23.7 5.79 15.1 21.9 25.4 12.8 1.63 4.13 1.25 2.35

Chone sp

## SDM for: chone_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -645.8 3768764 -0.0001714 0.9999
om -266.9 611132 -0.0004367 0.9997
gravel -134.3 743421 -0.0001806 0.9999
silt 19.72 357831 5.512e-05 1
clay 1.933 19767865 9.78e-08 1
arsenic -116.4 470332 -0.0002475 0.9998
cadmium 302.8 679239 0.0004459 0.9996
copper 422 875479 0.000482 0.9996
iron -592.8 1397448 -0.0004242 0.9997
manganese -137.3 757012 -0.0001814 0.9999
mercury 60.15 198975 0.0003023 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 18.7 1.36 5.04 1.12 8.45 18.9 29 29.7 2.96 2.61

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -71.01 140791 -0.0005043 0.9996
aquaculture 1.465 1423813 1.029e-06 1
city -161.8 1043763 -0.000155 0.9999
dredging_collect 3.309 1272201 2.601e-06 1
dredging_dump -26.73 1302138 -2.053e-05 1
industry 74.63 725722 0.0001028 0.9999
shipping_mooring -18.84 2029726 -9.282e-06 1
shipping_traffic -73.09 1305542 -5.599e-05 1
sewers_rain 30 1337511 2.243e-05 1
sewers_waste 84.55 1870889 4.519e-05 1
wharves_city 187 1966847 9.506e-05 0.9999
wharves_industry -75.07 1924373 -3.901e-05 1
fisheries_trap 7.911 79350 9.97e-05 0.9999
fisheries_trawl 4.787 214578 2.231e-05 1
fisheries_net 1.25 121769 1.026e-05 1
fisheries_dredge 0.9453 383211 2.467e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 18.6 22.8 26.7 27.9 15 24.3 26.1 16 21.8 48.2 38.9 5.76 3.25 1.3 2.72

Ciliatocardium ciliatum

## SDM for: ciliatocardium_ciliatum

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -264.6 440426 -0.0006009 0.9995
om 168.8 326096 0.0005176 0.9996
gravel -8.553 39461 -0.0002167 0.9998
silt -257.5 432674 -0.0005951 0.9995
clay 236.3 803942 0.0002939 0.9998
arsenic 92.71 416591 0.0002225 0.9998
cadmium -68.37 212492 -0.0003217 0.9997
copper 83.83 182890 0.0004584 0.9996
iron -85.57 226102 -0.0003785 0.9997
manganese -162.1 326948 -0.0004957 0.9996
mercury 51.08 201213 0.0002539 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 8.25 1.91 7.93 1.17 5.35 6.21 5.99 4.33 5.48 4.37

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -57.8 158431 -0.0003648 0.9997
aquaculture -141 2137836 -6.597e-05 0.9999
city -30.51 1621045 -1.882e-05 1
dredging_collect -20.2 3567286 -5.662e-06 1
dredging_dump 188 2816576 6.676e-05 0.9999
industry 27.12 954077 2.842e-05 1
shipping_mooring -129 1322614 -9.751e-05 0.9999
shipping_traffic -21.35 600520 -3.555e-05 1
sewers_rain 134.7 2486394 5.417e-05 1
sewers_waste -166.5 3425909 -4.861e-05 1
wharves_city 18.52 2582920 7.171e-06 1
wharves_industry -126.8 5303166 -2.392e-05 1
fisheries_trap -1.558 105641 -1.475e-05 1
fisheries_trawl -11.89 742876 -1.601e-05 1
fisheries_net 0.1052 116782 9.007e-07 1
fisheries_dredge -29.61 809373 -3.658e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 37.1 25 60.6 45.3 13.4 21.1 7.47 29.6 50.7 39.9 84.6 1.26 16.5 1.25 5.39

Cirripedia

## SDM for: cirripedia

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -44.22 75880 -0.0005827 0.9995
om -2.639 151441 -1.742e-05 1
gravel 1.01 63895 1.581e-05 1
silt -3.933 139094 -2.828e-05 1
clay 6.164 162601 3.791e-05 1
arsenic -8.429 294890 -2.858e-05 1
cadmium 5.174 98580 5.248e-05 1
copper -13.34 310807 -4.292e-05 1
iron -0.05162 54023 -9.556e-07 1
manganese 18.36 185661 9.89e-05 0.9999
mercury -5.739 218160 -2.63e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.31 1.25 3.05 1.13 3.36 3.87 7.01 1.59 3.89 3.24

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -60.17 130978 -0.0004594 0.9996
aquaculture 31.5 1380961 2.281e-05 1
city 37.95 1785017 2.126e-05 1
dredging_collect 63.15 742538 8.504e-05 0.9999
dredging_dump -63.26 1722768 -3.672e-05 1
industry 31.56 703594 4.485e-05 1
shipping_mooring 32.37 703891 4.598e-05 1
shipping_traffic 8 868298 9.213e-06 1
sewers_rain -15.99 2149623 -7.439e-06 1
sewers_waste 39.44 2782990 1.417e-05 1
wharves_city -59.64 1583439 -3.766e-05 1
wharves_industry -47.69 2079769 -2.293e-05 1
fisheries_trap 16.64 75018 0.0002218 0.9998
fisheries_trawl 0.4976 298217 1.669e-06 1
fisheries_net 2.283 109978 2.076e-05 1
fisheries_dredge -17.06 375642 -4.541e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 23 32.6 12.8 29.3 9.43 10.4 13 31.9 46.4 31.8 35.8 2.97 3.33 1.17 2.71

Cistenides granulata

## SDM for: cistenides_granulata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.21 
## Tjur's pseudo-R2 is: 0.19 
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.347 0.8041 -2.918 0.003518 * *
om 0.3262 0.6812 0.4788 0.6321
gravel 0.01923 0.3094 0.06214 0.9504
silt -0.3121 0.5791 -0.5389 0.5899
clay -1.299 3.723 -0.3489 0.7272
arsenic 0.6119 0.6969 0.8781 0.3799
cadmium -0.3133 0.5337 -0.5869 0.5573
copper -1.442 0.8214 -1.756 0.07911
iron -0.2667 0.4162 -0.6407 0.5217
manganese -0.1623 1.029 -0.1578 0.8746
mercury 0.134 0.6368 0.2105 0.8333 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.71 1.2 1.81 1.1 1.5 1.5 1.85 1.38 2.02 1.44

Influence indices

## McFadden's pseudo-R2 is: 0.38 
## Tjur's pseudo-R2 is: 0.38 
## Pearson's pseudo-R2 is: 0.39
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -75.65 10097 -0.007492 0.994
aquaculture -0.8221 3.684 -0.2232 0.8234
city -21.6 18.16 -1.19 0.2342
dredging_collect -0.8741 3.352 -0.2608 0.7942
dredging_dump -8.307 4.277 -1.942 0.05213
industry -0.02645 2.008 -0.01317 0.9895
shipping_mooring 9.744 7.712 1.264 0.2064
shipping_traffic 1.619 1.426 1.135 0.2564
sewers_rain 1.525 3.985 0.3827 0.7019
sewers_waste -12.43 9.685 -1.284 0.1992
wharves_city 17.15 14.9 1.151 0.2498
wharves_industry 8.75 4.704 1.86 0.06289
fisheries_trap 1.019 0.6782 1.503 0.133
fisheries_trawl 0.1398 0.3297 0.4239 0.6716
fisheries_net -727.7 104696 -0.00695 0.9945
fisheries_dredge -0.476 1.064 -0.4473 0.6547 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.5 43.6 9.14 10.7 4.61 21 3.64 9.91 23.2 37.6 12.1 1.19 1.53 1 1.84

Cossura longocirrata

## SDM for: cossura_longocirrata

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1045 1790518 -0.0005837 0.9995
om -204.3 4309757 -4.741e-05 1
gravel 335.4 1479060 0.0002267 0.9998
silt 748.7 2785928 0.0002688 0.9998
clay 133.7 3252924 4.111e-05 1
arsenic -28.47 882762 -3.225e-05 1
cadmium 360.8 616981 0.0005848 0.9995
copper -189.8 5559395 -3.414e-05 1
iron 392.3 4401443 8.913e-05 0.9999
manganese -120 2711133 -4.428e-05 1
mercury 26.94 3069482 8.777e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 102 6.38 36.1 3.06 29.4 15.1 194 82.1 48.6 47.9

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -74.33 124910 -0.000595 0.9995
aquaculture -34.27 2021987 -1.695e-05 1
city -154.8 1086070 -0.0001426 0.9999
dredging_collect 125.4 1054178 0.0001189 0.9999
dredging_dump 176.1 1367781 0.0001288 0.9999
industry 17.03 1424404 1.196e-05 1
shipping_mooring 136 1062050 0.000128 0.9999
shipping_traffic -64.24 776605 -8.272e-05 0.9999
sewers_rain 198.2 938698 0.0002111 0.9998
sewers_waste -284.7 1872163 -0.0001521 0.9999
wharves_city 70.9 1472122 4.816e-05 1
wharves_industry -296.6 1886161 -0.0001573 0.9999
fisheries_trap 6.957 97097 7.165e-05 0.9999
fisheries_trawl 7.799 101911 7.652e-05 0.9999
fisheries_net 0.767 110307 6.953e-06 1
fisheries_dredge -15.54 256295 -6.063e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 39.8 16.9 20.1 30.4 29.3 18.3 17.8 16.2 25.8 29.5 33.4 1.39 1.94 1.19 2.04

Crassicorophium bonellii

## SDM for: crassicorophium_bonellii

Abiotic parameters

## McFadden's pseudo-R2 is: 0.59 
## Tjur's pseudo-R2 is: 0.46 
## Pearson's pseudo-R2 is: 0.45
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -273.3 104039 -0.002627 0.9979
om -2.757 3.6 -0.7656 0.4439
gravel -0.4384 1.931 -0.2271 0.8203
silt 1.549 3.32 0.4667 0.6407
clay -1450 567722 -0.002554 0.998
arsenic 0.9015 2.878 0.3132 0.7541
cadmium -2.437 3.008 -0.8101 0.4179
copper -0.6514 3.089 -0.2109 0.833
iron -2.104 5.235 -0.4019 0.6878
manganese 5.197 4.167 1.247 0.2123
mercury -3 4.614 -0.6503 0.5155 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.89 1.18 2.96 1 2.48 3.01 3.71 3.85 4.52 2.89

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -62.84 127641 -0.0004923 0.9996
aquaculture 140.5 1256041 0.0001119 0.9999
city -27.7 1142978 -2.424e-05 1
dredging_collect -15.33 1257400 -1.219e-05 1
dredging_dump -83.28 753932 -0.0001105 0.9999
industry -63.59 495922 -0.0001282 0.9999
shipping_mooring 9.881 1196063 8.262e-06 1
shipping_traffic 6.234 625365 9.969e-06 1
sewers_rain -210.1 1528840 -0.0001375 0.9999
sewers_waste 308.3 1916854 0.0001608 0.9999
wharves_city 42.31 1324143 3.196e-05 1
wharves_industry 129.9 1778324 7.306e-05 0.9999
fisheries_trap -20.17 807226 -2.499e-05 1
fisheries_trawl 9.658 171792 5.622e-05 1
fisheries_net 10.06 183184 5.493e-05 1
fisheries_dredge 23.38 160823 0.0001454 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 18.1 18.2 23.3 15 8.9 18.6 12.3 32.7 38.8 20.1 35.8 4.16 2.89 1.97 1.74

Crenella decussata

## SDM for: crenella_decussata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.78 
## Tjur's pseudo-R2 is: 0.76 
## Pearson's pseudo-R2 is: 0.76
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -29.8 22.83 -1.305 0.1918
om -13.5 11.14 -1.212 0.2255
gravel 3.567 2.154 1.656 0.0977
silt 2.274 2.76 0.8238 0.41
clay 5.278 5.165 1.022 0.3068
arsenic -15.11 16.08 -0.94 0.3472
cadmium 0.3276 2.559 0.128 0.8981
copper 2.786 4.842 0.5754 0.565
iron 1.539 3.441 0.4472 0.6548
manganese -10.59 17.55 -0.6033 0.5463
mercury -6.099 6.397 -0.9533 0.3404 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.02 1.28 3.16 2.18 2.18 2.31 2.47 2.55 3.93 3.83

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -620.2 1458329 -0.0004253 0.9997
aquaculture -176.6 6367453 -2.774e-05 1
city 405.7 9846434 4.121e-05 1
dredging_collect 665.1 3658749 0.0001818 0.9999
dredging_dump 213.5 2723940 7.839e-05 0.9999
industry -308.9 2495149 -0.0001238 0.9999
shipping_mooring 1023 9593856 0.0001066 0.9999
shipping_traffic 285 1342746 0.0002122 0.9998
sewers_rain 1324 4396795 0.0003011 0.9998
sewers_waste -3048 7405218 -0.0004116 0.9997
wharves_city -1169 8128917 -0.0001438 0.9999
wharves_industry -332.7 2059690 -0.0001615 0.9999
fisheries_trap 31.53 169080 0.0001865 0.9999
fisheries_trawl -44.55 871479 -5.112e-05 1
fisheries_net 3.088 6463619 4.777e-07 1
fisheries_dredge -96.21 497787 -0.0001933 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 109 163 68.8 45.2 31.1 159 31.9 102 124 141 38.4 1.95 9.03 1.28 4.94

Cumacea

## SDM for: cumacea

Abiotic parameters

## McFadden's pseudo-R2 is: 0.17 
## Tjur's pseudo-R2 is: 0.02 
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -128 42198 -0.003033 0.9976
om -0.6183 2.356 -0.2624 0.793
gravel -30.29 28362 -0.001068 0.9991
silt -0.3444 2.726 -0.1264 0.8995
clay -627.2 226212 -0.002772 0.9978
arsenic 0.3619 1.283 0.2821 0.7779
cadmium -0.9563 1.739 -0.5499 0.5824
copper 0.1673 1.959 0.08537 0.932
iron -0.652 1.968 -0.3313 0.7405
manganese 1.86 2.565 0.725 0.4685
mercury -0.5423 2.105 -0.2577 0.7967 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.01 1 2.7 1 1.59 1.82 2.33 1.76 3.08 2.5

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -61.82 145646 -0.0004245 0.9997
aquaculture 203.4 1745366 0.0001166 0.9999
city 15.68 1548200 1.013e-05 1
dredging_collect 38.44 812731 4.73e-05 1
dredging_dump -70.16 1376180 -5.098e-05 1
industry -76.12 506779 -0.0001502 0.9999
shipping_mooring 104.7 1157996 9.045e-05 0.9999
shipping_traffic 28.66 613197 4.673e-05 1
sewers_rain -100.6 1264314 -7.96e-05 0.9999
sewers_waste 171.1 1535284 0.0001114 0.9999
wharves_city -41.19 1465481 -2.81e-05 1
wharves_industry 30.04 1519005 1.978e-05 1
fisheries_trap 3.99 92121 4.331e-05 1
fisheries_trawl -9.307 245987 -3.784e-05 1
fisheries_net -1.207 106662 -1.131e-05 1
fisheries_dredge -34.73 1003265 -3.461e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 24.7 25.4 15.2 23.5 7.39 18.6 9.61 29.4 28.7 23.5 29 1.29 2.59 1.09 5.14

Cyclocardia borealis

## SDM for: cyclocardia_borealis

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Cyrtodaria siliqua

## SDM for: cyrtodaria_siliqua

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -748.1 1541191 -0.0004854 0.9996
om -388.7 893766 -0.0004349 0.9997
gravel -92.15 593759 -0.0001552 0.9999
silt 31.2 353157 8.834e-05 0.9999
clay 126 341223 0.0003691 0.9997
arsenic -264.5 1888482 -0.00014 0.9999
cadmium 123.6 545310 0.0002267 0.9998
copper 165.2 550194 0.0003003 0.9998
iron 85.97 228157 0.0003768 0.9997
manganese -287.9 1147693 -0.0002508 0.9998
mercury -320.8 1170193 -0.0002741 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 8.2 1.5 3.21 3.34 7.22 8.01 7.1 11.9 5.86 10.4

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -118.3 403879 -0.0002928 0.9998
aquaculture -416 11032501 -3.771e-05 1
city 120.9 13647458 8.857e-06 1
dredging_collect -248.8 3656150 -6.806e-05 0.9999
dredging_dump 176.9 5986096 2.955e-05 1
industry 99.44 3294415 3.018e-05 1
shipping_mooring -268.5 11695926 -2.296e-05 1
shipping_traffic -31.74 5292898 -5.997e-06 1
sewers_rain 293 6230508 4.703e-05 1
sewers_waste -440.9 4792057 -9.201e-05 0.9999
wharves_city -66.45 16516282 -4.023e-06 1
wharves_industry 143.9 4616027 3.118e-05 1
fisheries_trap -23.46 598903 -3.917e-05 1
fisheries_trawl 19.14 487639 3.924e-05 1
fisheries_net 9.298 313267 2.968e-05 1
fisheries_dredge 82.97 1415017 5.864e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 101 188 48.5 80.6 31.8 94.8 71.2 44.7 31.9 253 64 2.79 3.71 2.04 11

Diastylis rathkei

## SDM for: diastylis_rathkei

Abiotic parameters

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.24 
## Pearson's pseudo-R2 is: 0.22
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.075 1.011 -4.031 5.551e-05 * * *
om -2.186 1.047 -2.088 0.03678 *
gravel 1.086 0.5487 1.98 0.04775 *
silt 2.624 1.2 2.186 0.02883 *
clay -1.298 1.539 -0.8431 0.3992
arsenic -1.44 1.627 -0.8853 0.376
cadmium -0.4527 0.7203 -0.6286 0.5296
copper 0.895 0.9132 0.98 0.3271
iron 0.8496 0.5164 1.645 0.0999
manganese 1.016 1.383 0.7346 0.4626
mercury -1.933 1.12 -1.727 0.08417 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.6 1.76 2.56 1.09 1.9 1.66 1.88 1.56 2.4 1.69

Influence indices

## McFadden's pseudo-R2 is: 0.22 
## Tjur's pseudo-R2 is: 0.17 
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -45.3 11414 -0.003969 0.9968
aquaculture 2.949 4.948 0.596 0.5512
city 0.656 4.651 0.1411 0.8878
dredging_collect -3.011 3.831 -0.786 0.4319
dredging_dump 1.417 4.441 0.319 0.7497
industry -2.65 2.041 -1.298 0.1943
shipping_mooring -1.062 3.997 -0.2658 0.7904
shipping_traffic 0.5321 1.399 0.3802 0.7038
sewers_rain -4.253 5.296 -0.8032 0.4219
sewers_waste 3.569 7.306 0.4885 0.6252
wharves_city -1.439 5.072 -0.2836 0.7767
wharves_industry 4.919 5.979 0.8228 0.4106
fisheries_trap 0.04892 0.4417 0.1108 0.9118
fisheries_trawl -0.404 0.957 -0.4221 0.6729
fisheries_net -431.6 118352 -0.003647 0.9971
fisheries_dredge -3.246 2.198 -1.477 0.1397 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.2 12.9 10.3 12.1 4.06 9.18 3.33 11.4 16.2 14.8 15.8 1.12 2.22 1 2.2

Diastylis sculpta

## SDM for: diastylis_sculpta

Abiotic parameters

## McFadden's pseudo-R2 is: 0.39 
## Tjur's pseudo-R2 is: 0.29 
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.761 2.047 -2.815 0.004881 * *
om -2.685 1.87 -1.436 0.151
gravel -0.3701 1.733 -0.2136 0.8309
silt 2.21 1.432 1.542 0.123
clay -0.9117 1.524 -0.5983 0.5496
arsenic -2.924 3.153 -0.9273 0.3538
cadmium -1.7 1.554 -1.094 0.2738
copper 3.982 1.801 2.211 0.02703 *
iron -0.997 1.617 -0.6166 0.5375
manganese 0.2236 2.921 0.07655 0.939
mercury -2.189 1.617 -1.353 0.176 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.88 1.22 1.97 1.14 2.09 2.45 3.24 2.37 3.11 1.78

Influence indices

## McFadden's pseudo-R2 is: -8.94 
## Tjur's pseudo-R2 is: 0 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.398e+15 7496963 -319830134 0 * * *
aquaculture -4.774e+13 66771996 -714971 0 * * *
city -3.179e+15 60515998 -52525110 0 * * *
dredging_collect 1.988e+14 47886179 4152477 0 * * *
dredging_dump -2.275e+15 55589796 -40929096 0 * * *
industry -2.388e+14 30193191 -7909223 0 * * *
shipping_mooring 1.399e+15 51224704 27302347 0 * * *
shipping_traffic -1.823e+15 22815885 -79887457 0 * * *
sewers_rain -1.824e+14 67164046 -2716297 0 * * *
sewers_waste -1.205e+15 90359677 -13337312 0 * * *
wharves_city 4.18e+15 72465526 57680109 0 * * *
wharves_industry 2.988e+15 79067645 37784780 0 * * *
fisheries_trap 2.115e+13 7277539 2906037 0 * * *
fisheries_trawl 2.704e+14 8821984 30650023 0 * * *
fisheries_net 2.984e+14 7219163 41337019 0 * * *
fisheries_dredge -2.207e+14 19454028 -11342147 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Diastylis sp

## SDM for: diastylis_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.57 
## Tjur's pseudo-R2 is: 0.23 
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.82 13531 -0.001982 0.9984
om 1.916 5.64 0.3397 0.7341
gravel -14.38 15078 -0.0009537 0.9992
silt 3.138 6.532 0.4805 0.6309
clay -60.03 70639 -0.0008499 0.9993
arsenic -3.082 23.26 -0.1325 0.8946
cadmium 1.466 4.203 0.3489 0.7272
copper -0.5717 5.698 -0.1003 0.9201
iron 0.8747 3.056 0.2862 0.7747
manganese -5.513 12.65 -0.4357 0.663
mercury -3.01 5.84 -0.5153 0.6063 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.45 1 1.99 1 3.19 2.07 2.36 1.64 2 1.75

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -116.1 347694 -0.0003338 0.9997
aquaculture -62.12 8744652 -7.104e-06 1
city 75.52 4601540 1.641e-05 1
dredging_collect -243.4 8020177 -3.035e-05 1
dredging_dump 236.4 6990674 3.382e-05 1
industry 42.81 2759877 1.551e-05 1
shipping_mooring -63.9 8e+06 -7.987e-06 1
shipping_traffic -148.2 1241730 -0.0001193 0.9999
sewers_rain 235 6691602 3.511e-05 1
sewers_waste -246.2 7680325 -3.205e-05 1
wharves_city -112.3 6308112 -1.78e-05 1
wharves_industry 140.7 14723296 9.553e-06 1
fisheries_trap 9.174 723965 1.267e-05 1
fisheries_trawl 10.42 457603 2.277e-05 1
fisheries_net 4.843 474452 1.021e-05 1
fisheries_dredge -98.64 2034794 -4.848e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 121 43.1 81 65.7 23.8 96.7 13.8 82.5 101 57.5 142 8.76 10.7 3.09 21.6

Echinarachnius parma

## SDM for: echinarachnius_parma

Abiotic parameters

## McFadden's pseudo-R2 is: 0.62 
## Tjur's pseudo-R2 is: 0.63 
## Pearson's pseudo-R2 is: 0.65
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -18.89 6.594 -2.865 0.004172 * *
om -6.29 2.743 -2.293 0.02183 *
gravel 0.02614 1.03 0.02537 0.9798
silt 3.289 1.397 2.354 0.01856 *
clay 0.6205 2.414 0.2571 0.7971
arsenic -17.82 6.984 -2.551 0.01073 *
cadmium 2.074 1.373 1.51 0.1309
copper 2.689 1.383 1.944 0.05195
iron 1.318 0.7683 1.716 0.08617
manganese -7.79 3.967 -1.964 0.04959 *
mercury -5.603 2.383 -2.352 0.01869 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.48 1.07 2.49 1.07 1.79 2.94 1.91 2.71 3.61 2.61

Influence indices

## McFadden's pseudo-R2 is: 0.56 
## Tjur's pseudo-R2 is: 0.54 
## Pearson's pseudo-R2 is: 0.54
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -67.82 11393 -0.005953 0.9953
aquaculture -2.188 6.429 -0.3403 0.7336
city 7.769 8.32 0.9337 0.3504
dredging_collect 2.362 4.245 0.5564 0.5779
dredging_dump 6.501 9.892 0.6572 0.511
industry -11.41 5.226 -2.184 0.02899 *
shipping_mooring -1.655 6.445 -0.2568 0.7974
shipping_traffic -2.284 1.961 -1.165 0.2441
sewers_rain 12.38 9.564 1.295 0.1955
sewers_waste -27.36 14.87 -1.84 0.06572
wharves_city -12.11 11.85 -1.021 0.3072
wharves_industry 9.522 6.437 1.479 0.139
fisheries_trap 0.5223 0.4497 1.161 0.2455
fisheries_trawl 0.6427 0.436 1.474 0.1404
fisheries_net -614.1 118136 -0.005198 0.9959
fisheries_dredge -2.804 1.682 -1.667 0.09552 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.85 21.6 10.8 25.5 9.68 12.1 4.49 19 25 31.7 15.3 1.51 1.73 1 2.36

Edotia montosa

## SDM for: edotia_montosa

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -146.9 360275 -0.0004078 0.9997
om 143.9 414409 0.0003472 0.9997
gravel -52.99 318008 -0.0001666 0.9999
silt -168.1 469961 -0.0003577 0.9997
clay 124 698292 0.0001776 0.9999
arsenic -43.51 413629 -0.0001052 0.9999
cadmium 33.21 133483 0.0002488 0.9998
copper 45.85 318868 0.0001438 0.9999
iron -118.3 520671 -0.0002272 0.9998
manganese 105.4 356874 0.0002954 0.9998
mercury -63.9 424385 -0.0001506 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 6.44 6.61 5.52 1.14 6.04 2.78 8.54 8.14 7.47 5.57

Influence indices

## McFadden's pseudo-R2 is: 0.38 
## Tjur's pseudo-R2 is: 0.22 
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -164.3 84226 -0.001951 0.9984
aquaculture -17.95 20.12 -0.892 0.3724
city 0.2005 9.749 0.02056 0.9836
dredging_collect -6.436 11.96 -0.5379 0.5906
dredging_dump -9.312 8.776 -1.061 0.2886
industry 7.693 8.381 0.9179 0.3587
shipping_mooring -10.72 11.23 -0.9544 0.3399
shipping_traffic -2.921 8.044 -0.3631 0.7165
sewers_rain 6.065 12.86 0.4715 0.6373
sewers_waste -8.253 16.98 -0.4861 0.6269
wharves_city 6.759 15.86 0.4262 0.6699
wharves_industry 14.07 16.19 0.8693 0.3847
fisheries_trap -12.17 14.27 -0.8529 0.3937
fisheries_trawl -128.5 1002382 -0.0001282 0.9999
fisheries_net -827.7 293735 -0.002818 0.9978
fisheries_dredge -117.4 594522 -0.0001975 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 20.6 15.9 21.5 16.9 20.1 9.32 17.7 13.4 17.5 25 32 2.11 5.82 1 5.82

Ennucula tenuis

## SDM for: ennucula_tenuis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.29 
## Tjur's pseudo-R2 is: 0.35 
## Pearson's pseudo-R2 is: 0.37
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.4074 0.4181 -0.9744 0.3298
om 1.057 0.6095 1.735 0.08281
gravel 0.2424 0.3266 0.7424 0.4579
silt 0.202 0.6277 0.3218 0.7476
clay -0.8662 1.835 -0.4719 0.637
arsenic 2.114 0.8527 2.479 0.01319 *
cadmium -2.316 0.7399 -3.13 0.001746 * *
copper 0.03025 0.603 0.05018 0.96
iron -0.08138 0.3688 -0.2207 0.8253
manganese -2.155 0.8008 -2.691 0.007127 * *
mercury 1.1 0.6251 1.76 0.07846 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.33 1.47 2.42 1.03 2.25 2.49 2.17 1.42 2.69 2.2

Influence indices

## McFadden's pseudo-R2 is: 0.4 
## Tjur's pseudo-R2 is: 0.45 
## Pearson's pseudo-R2 is: 0.45
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2322 0.6606 -0.3514 0.7253
aquaculture 2.744 3.367 0.815 0.4151
city -0.8301 4.664 -0.178 0.8587
dredging_collect 1.24 2.238 0.5539 0.5796
dredging_dump -3.695 2.31 -1.6 0.1097
industry 1.927 1.264 1.525 0.1274
shipping_mooring 1.902 2.59 0.7343 0.4627
shipping_traffic 2.051 1.032 1.988 0.0468 *
sewers_rain -3.409 2.647 -1.288 0.1978
sewers_waste 4.558 3.955 1.153 0.2491
wharves_city 1.77 4.516 0.3918 0.6952
wharves_industry -0.9432 3.353 -0.2813 0.7785
fisheries_trap -0.1131 0.3778 -0.2994 0.7646
fisheries_trawl -0.1095 0.3018 -0.3629 0.7167
fisheries_net 0.8427 3.981 0.2117 0.8323
fisheries_dredge 2.22 1.335 1.662 0.09643 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11 15.6 7.63 7.98 3.16 9.01 3.23 9.04 13.6 15.8 11.3 1.07 1.43 1 2.01

Eteone sp

## SDM for: eteone_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.19 
## Tjur's pseudo-R2 is: 0.1 
## Pearson's pseudo-R2 is: 0.1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -369.5 57199 -0.00646 0.9948
om -2.116 1.229 -1.722 0.08508
gravel 0.4254 0.4622 0.9204 0.3574
silt 0.9025 0.9995 0.9029 0.3666
clay -1998 312123 -0.0064 0.9949
arsenic 0.3058 0.7581 0.4034 0.6867
cadmium -0.6232 1.017 -0.613 0.5399
copper 0.8963 1.219 0.735 0.4623
iron 0.2086 0.6309 0.3307 0.7409
manganese -0.3976 1.35 -0.2945 0.7684
mercury -0.2233 1.253 -0.1783 0.8585 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.57 1.4 2.1 1 1.6 1.85 2.51 1.43 2.35 1.75

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1108 1792834 -0.000618 0.9995
aquaculture 387.9 2955483 0.0001313 0.9999
city -1439 12678348 -0.0001135 0.9999
dredging_collect -2163 7965912 -0.0002716 0.9998
dredging_dump -1514 5613722 -0.0002698 0.9998
industry 101.7 4086051 2.488e-05 1
shipping_mooring -305.2 6037817 -5.055e-05 1
shipping_traffic -96.43 2983314 -3.232e-05 1
sewers_rain -704.8 2660942 -0.0002649 0.9998
sewers_waste 1386 10077376 0.0001376 0.9999
wharves_city 2337 13700047 0.0001706 0.9999
wharves_industry 2766 13868615 0.0001994 0.9998
fisheries_trap -9.648 285588 -3.378e-05 1
fisheries_trawl -5.294 584983 -9.05e-06 1
fisheries_net -4263 6443032 -0.0006616 0.9995
fisheries_dredge -402 5399278 -7.446e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 54.2 242 158 93 47.4 140 28 53.6 186 259 254 6.01 43.4 1.58 7.02

Euchone sp

## SDM for: euchone_sp

Abiotic parameters

## McFadden's pseudo-R2 is: -12.23 
## Tjur's pseudo-R2 is: -0.01 
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.301e+15 7978909 -413766906 0 * * *
om -1.493e+15 14257309 -104685824 0 * * *
gravel 7.237e+14 8714549 83047230 0 * * *
silt 1.384e+15 15274819 90612828 0 * * *
clay -7.527e+14 21858102 -34434528 0 * * *
arsenic -1.752e+15 11951982 -146562517 0 * * *
cadmium 6.267e+14 10400421 60260861 0 * * *
copper 1.939e+15 13687347 141670271 0 * * *
iron -4.869e+14 9620115 -50615564 0 * * *
manganese -5.417e+14 14288707 -37912203 0 * * *
mercury -6.123e+14 12068606 -50738739 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -108.2 230852 -0.0004686 0.9996
aquaculture -372.7 7074900 -5.267e-05 1
city -426.4 3308637 -0.0001289 0.9999
dredging_collect -235.4 21696254 -1.085e-05 1
dredging_dump -82.52 6321537 -1.305e-05 1
industry 213.3 1182221 0.0001805 0.9999
shipping_mooring -280.9 11462006 -2.451e-05 1
shipping_traffic -98.85 3244147 -3.047e-05 1
sewers_rain 103.2 6422833 1.607e-05 1
sewers_waste -34.09 8749498 -3.896e-06 1
wharves_city 620.2 4779428 0.0001298 0.9999
wharves_industry 91.44 28604720 3.197e-06 1
fisheries_trap -14.2 464038 -3.06e-05 1
fisheries_trawl 11.79 560768 2.102e-05 1
fisheries_net 5.979 272327 2.195e-05 1
fisheries_dredge 40.26 6081530 6.621e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 58.6 47.9 255 80.8 13 103 34.1 46.3 61.4 78.8 312 9.76 3.81 1.77 36.1

Eudorella emarginata

## SDM for: eudorella_emarginata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.41 
## Tjur's pseudo-R2 is: 0.28 
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -258.5 60111 -0.0043 0.9966
om -0.09617 0.9278 -0.1037 0.9174
gravel 1.322 0.8811 1.5 0.1335
silt 2.358 1.743 1.353 0.1761
clay -1383 328018 -0.004215 0.9966
arsenic -0.1808 0.7264 -0.2488 0.8035
cadmium 1.1 1.239 0.8879 0.3746
copper -0.1378 2.327 -0.05919 0.9528
iron -0.1505 4.708 -0.03197 0.9745
manganese -0.0414 1.582 -0.02617 0.9791
mercury 0.6286 0.8325 0.7551 0.4502 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.69 2.15 2.27 1 1.48 1.92 4.71 6.57 3.16 1.66

Influence indices

## McFadden's pseudo-R2 is: 0.74 
## Tjur's pseudo-R2 is: 0.72 
## Pearson's pseudo-R2 is: 0.74
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -143.1 1106 -0.1294 0.8971
aquaculture -75.89 79.81 -0.9508 0.3417
city -60.28 60.97 -0.9887 0.3228
dredging_collect 150.8 73.68 2.047 0.04069 *
dredging_dump 38.11 28.91 1.318 0.1875
industry -4.363 14.39 -0.3031 0.7618
shipping_mooring 57.16 30.59 1.869 0.06166
shipping_traffic 16.95 28.49 0.5949 0.5519
sewers_rain -36.14 45.7 -0.7908 0.429
sewers_waste 116.2 80.23 1.449 0.1474
wharves_city 60.15 58.98 1.02 0.3078
wharves_industry -236.1 122.1 -1.934 0.05317
fisheries_trap -7.938 8.601 -0.9229 0.356
fisheries_trawl 45.32 2896 0.01565 0.9875
fisheries_net 16.96 7631 0.002222 0.9982
fisheries_dredge 78.34 879.1 0.08911 0.929 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 23.1 39.7 46 18.4 11.3 6.68 19.5 22.2 29.9 42 77.2 1.55 1 1 1

Eudorellopsis integra

## SDM for: eudorellopsis_integra

Abiotic parameters

## McFadden's pseudo-R2 is: 0.42 
## Tjur's pseudo-R2 is: 0.47 
## Pearson's pseudo-R2 is: 0.47
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.425 26.92 -0.2758 0.7827
om 2.734 0.8385 3.261 0.001111 * *
gravel -0.4487 0.4092 -1.097 0.2728
silt -0.5124 0.5691 -0.9003 0.3679
clay -45.45 147.3 -0.3086 0.7577
arsenic 0.6001 0.9013 0.6659 0.5055
cadmium -0.8911 0.444 -2.007 0.04476 *
copper -1.537 0.6382 -2.408 0.01605 *
iron -1.094 0.5621 -1.946 0.0516
manganese 1.956 0.8746 2.236 0.02534 *
mercury 0.5882 0.6548 0.8983 0.369 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.95 1.14 1.69 1.03 1.81 1.48 2.03 1.61 2.06 1.55

Influence indices

## McFadden's pseudo-R2 is: 0.84 
## Tjur's pseudo-R2 is: 0.86 
## Pearson's pseudo-R2 is: 0.87
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -13.43 37.49 -0.3582 0.7202
aquaculture -8.001 11.42 -0.7005 0.4836
city -279.4 149.3 -1.871 0.06128
dredging_collect 54.43 40.1 1.357 0.1746
dredging_dump -33.82 26.22 -1.29 0.1971
industry 21.33 12.47 1.71 0.0872
shipping_mooring 74.41 41.56 1.79 0.07338
shipping_traffic 18.9 11.86 1.594 0.111
sewers_rain -32.57 22.67 -1.437 0.1508
sewers_waste -5.669 15.37 -0.3687 0.7123
wharves_city 249.2 133.6 1.865 0.06221
wharves_industry -59.34 44.17 -1.344 0.1791
fisheries_trap -1.357 3.133 -0.433 0.665
fisheries_trawl -1.957 2.44 -0.8021 0.4225
fisheries_net -1.25 380.3 -0.003287 0.9974
fisheries_dredge 11.26 8.824 1.276 0.202 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 17.5 264 74.5 49.8 19.7 66.5 20.3 36.9 22.6 250 79.5 2.78 4.35 1 9.42

Euspira pallida

## SDM for: euspira_pallida

Abiotic parameters

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.05 
## Pearson's pseudo-R2 is: 0.03
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -110.1 55630 -0.001978 0.9984
om -1.751 4.216 -0.4154 0.6778
gravel -27.16 46141 -0.0005886 0.9995
silt 1.672 3.353 0.4987 0.618
clay -521.1 295859 -0.001761 0.9986
arsenic 0.6377 3.319 0.1921 0.8477
cadmium -1.961 3.269 -0.5999 0.5486
copper 1.817 3.22 0.5644 0.5725
iron -1.49 4.276 -0.3486 0.7274
manganese -0.5405 8.859 -0.06101 0.9514
mercury -2.486 4.851 -0.5125 0.6083 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.99 1 2.65 1 1.43 1.64 1.88 2.5 2.72 2.43

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -270.8 653366 -0.0004145 0.9997
aquaculture -685.8 3347015 -0.0002049 0.9998
city -63.64 2663918 -2.389e-05 1
dredging_collect 173.9 4035131 4.309e-05 1
dredging_dump 1255 5621187 0.0002232 0.9998
industry 259.8 1740241 0.0001493 0.9999
shipping_mooring -561.2 6040433 -9.291e-05 0.9999
shipping_traffic -122 1919311 -6.359e-05 0.9999
sewers_rain 562.5 8546065 6.582e-05 0.9999
sewers_waste -724.1 11184571 -6.474e-05 0.9999
wharves_city -9.422 5592519 -1.685e-06 1
wharves_industry -1297 9453606 -0.0001372 0.9999
fisheries_trap -41.32 1640425 -2.519e-05 1
fisheries_trawl -120.4 2698757 -4.46e-05 1
fisheries_net 38.96 748492 5.206e-05 1
fisheries_dredge -72.49 248939 -0.0002912 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 36.4 37.4 53.5 85.6 20.2 89 32.6 144 175 80.4 140 2.78 20.3 2.96 7.21

Glycera capitata

## SDM for: glycera_capitata

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -109.4 282149 -0.0003877 0.9997
om -74.72 3625175 -2.061e-05 1
gravel 32.36 254640 0.0001271 0.9999
silt 37.08 1909038 1.942e-05 1
clay 13.8 1278124 1.08e-05 1
arsenic -26.66 6007963 -4.437e-06 1
cadmium 13.47 4288346 3.142e-06 1
copper 47.5 2057839 2.308e-05 1
iron 15.74 3857034 4.08e-06 1
manganese -23.74 2391369 -9.928e-06 1
mercury 16.82 5294634 3.177e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 41.3 10.8 37.4 21.4 55.5 75.2 30.9 32.5 30.9 72.2

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -71.64 124295 -0.0005764 0.9995
aquaculture 30.96 666058 4.648e-05 1
city 92.5 1376704 6.719e-05 0.9999
dredging_collect 61.81 512461 0.0001206 0.9999
dredging_dump -118.9 933669 -0.0001273 0.9999
industry 74.5 406565 0.0001832 0.9999
shipping_mooring 43.49 454021 9.578e-05 0.9999
shipping_traffic 27.12 267895 0.0001013 0.9999
sewers_rain 28.61 826541 3.462e-05 1
sewers_waste -9.756 1423256 -6.855e-06 1
wharves_city -118.8 1898845 -6.256e-05 1
wharves_industry -53.03 801086 -6.62e-05 0.9999
fisheries_trap 0.9245 260829 3.544e-06 1
fisheries_trawl -6.025 45901 -0.0001313 0.9999
fisheries_net -0.2387 87858 -2.717e-06 1
fisheries_dredge -2.371 268087 -8.844e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.5 25.1 14 20.2 9.71 9.25 5.28 21.7 28.4 34.3 20.3 2.5 2.01 1.01 1.62

Glycera sp

## SDM for: glycera_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.61 
## Tjur's pseudo-R2 is: 0.46 
## Pearson's pseudo-R2 is: 0.44
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -28.5 19.47 -1.464 0.1433
om -17 12.26 -1.387 0.1653
gravel 8.449 6.468 1.306 0.1915
silt 10.68 7.723 1.382 0.1669
clay -3.516 11.28 -0.3117 0.7553
arsenic -10.01 7.086 -1.412 0.1578
cadmium 14.58 10.54 1.383 0.1667
copper 18.99 13.83 1.374 0.1696
iron -20.27 16.92 -1.198 0.231
manganese -2.114 3.629 -0.5827 0.5601
mercury 2.269 3.142 0.7223 0.4701 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 18.7 8.03 8.09 1.01 8.54 19.2 28.1 19.7 2.76 2.27

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1024 2360957 -0.0004336 0.9997
aquaculture -100.5 2159497 -4.653e-05 1
city -128.8 3385554 -3.804e-05 1
dredging_collect 500.3 6257509 7.995e-05 0.9999
dredging_dump 2748 7067536 0.0003889 0.9997
industry -1009 3261005 -0.0003093 0.9998
shipping_mooring 574.3 1819872 0.0003156 0.9997
shipping_traffic -1564 4039732 -0.0003872 0.9997
sewers_rain 1821 6379893 0.0002855 0.9998
sewers_waste -2684 10348060 -0.0002594 0.9998
wharves_city -907.5 4746808 -0.0001912 0.9998
wharves_industry -797.1 7109287 -0.0001121 0.9999
fisheries_trap -207.6 929163 -0.0002234 0.9998
fisheries_trawl 246 567331 0.0004335 0.9997
fisheries_net 120.1 6464650 1.857e-05 1
fisheries_dredge -381.2 5280187 -7.22e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 30.5 66.6 81.2 97.6 52.2 19.4 53 99.8 142 81.9 92.7 1.82 2.62 1 24.1

Goniada maculata

## SDM for: goniada_maculata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.23 
## Tjur's pseudo-R2 is: 0.25 
## Pearson's pseudo-R2 is: 0.25
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -360.8 48599 -0.007425 0.9941
om 0.843 0.5315 1.586 0.1127
gravel -1.176 0.7981 -1.473 0.1407
silt -0.06688 0.5551 -0.1205 0.9041
clay -1966 265195 -0.007415 0.9941
arsenic 0.5867 0.4407 1.331 0.1831
cadmium -0.1808 0.3781 -0.4783 0.6324
copper -0.9318 0.4928 -1.891 0.05865
iron -0.1539 0.3115 -0.4942 0.6212
manganese -0.5916 0.5924 -0.9988 0.3179
mercury 0.2728 0.4396 0.6207 0.5348 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.01 1.1 2.11 1 1.52 1.48 1.8 1.36 1.86 1.56

Influence indices

## McFadden's pseudo-R2 is: 0.47 
## Tjur's pseudo-R2 is: 0.5 
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.38 3.603 -2.88 0.003977 * *
aquaculture 3.918 3.21 1.22 0.2223
city -82.15 28.41 -2.891 0.003836 * *
dredging_collect 9.15 3.434 2.664 0.00772 * *
dredging_dump -14.18 5.521 -2.568 0.01022 *
industry -1.904 1.411 -1.35 0.1772
shipping_mooring 31.17 10.21 3.052 0.002271 * *
shipping_traffic 4.293 1.989 2.159 0.03087 *
sewers_rain -3.46 3.597 -0.962 0.336
sewers_waste -19.26 8.009 -2.405 0.01617 *
wharves_city 66.8 23.39 2.856 0.004284 * *
wharves_industry 7.103 4.893 1.452 0.1466
fisheries_trap -0.3121 0.6213 -0.5024 0.6154
fisheries_trawl 1.743 0.7565 2.303 0.02126 *
fisheries_net 0.9809 4.91 0.1998 0.8417
fisheries_dredge 1.498 1.215 1.233 0.2176 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.1 81 10.7 16.2 2.83 33.7 5.79 11.7 26.9 71.8 14.8 1.17 2.03 1 2.13

Guernea prinassus nordenskioldi

## SDM for: guernea_prinassus_nordenskioldi

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Halacaridae spp

## SDM for: halacaridae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -182.6 473133 -0.0003859 0.9997
om -134.2 358347 -0.0003744 0.9997
gravel -4.482 286106 -1.566e-05 1
silt 64.12 285563 0.0002245 0.9998
clay -105.7 1398946 -7.555e-05 0.9999
arsenic 5.142 431968 1.19e-05 1
cadmium -40.83 219759 -0.0001858 0.9999
copper 62.63 258197 0.0002426 0.9998
iron 9.686 117058 8.274e-05 0.9999
manganese -19.25 328394 -5.861e-05 1
mercury -69.44 301137 -0.0002306 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.88 1.28 4.22 1.51 3.79 3.13 4.32 1.92 4.01 3.3

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -35.16 72323 -0.0004862 0.9996
aquaculture -24 1358385 -1.767e-05 1
city -12.59 1297078 -9.708e-06 1
dredging_collect -14.92 851895 -1.751e-05 1
dredging_dump -6.445 662645 -9.726e-06 1
industry 11.9 525131 2.266e-05 1
shipping_mooring -27.69 1438980 -1.924e-05 1
shipping_traffic -18.11 338086 -5.356e-05 1
sewers_rain -19.75 1022136 -1.932e-05 1
sewers_waste 24.54 1349796 1.818e-05 1
wharves_city 28.32 1807498 1.567e-05 1
wharves_industry 31.49 777801 4.048e-05 1
fisheries_trap -0.6731 83030 -8.106e-06 1
fisheries_trawl 10.88 67868 0.0001604 0.9999
fisheries_net 3.765 70648 5.329e-05 1
fisheries_dredge 0.6407 190947 3.355e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 27.5 18.2 14.2 11 9.45 23.7 5.79 15.1 21.9 25.4 12.8 1.63 4.13 1.25 2.35

Haminoea solitaria

## SDM for: haminoea_solitaria

Abiotic parameters

## McFadden's pseudo-R2 is: 0.4 
## Tjur's pseudo-R2 is: 0.09 
## Pearson's pseudo-R2 is: 0.06
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -28.33 7334 -0.003863 0.9969
om 2.883 8.032 0.3589 0.7196
gravel -26.04 11468 -0.002271 0.9982
silt -1.792 4.549 -0.3939 0.6936
clay -48.61 35981 -0.001351 0.9989
arsenic -1.186 25.9 -0.04579 0.9635
cadmium 1.978 4.607 0.4293 0.6677
copper -0.6456 6.62 -0.09751 0.9223
iron -2.897 11.07 -0.2618 0.7935
manganese -4.971 18.11 -0.2745 0.7837
mercury -3.972 8.918 -0.4454 0.656 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 6.05 1 4.36 1 4.03 3.02 2.08 2.48 2.39 3.25

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -140.7 328669 -0.0004281 0.9997
aquaculture 229.6 10937180 2.1e-05 1
city 273.9 8728790 3.137e-05 1
dredging_collect -365.4 6986522 -5.23e-05 1
dredging_dump -153.6 3458238 -4.441e-05 1
industry -59.61 4474646 -1.332e-05 1
shipping_mooring 167.9 8742986 1.92e-05 1
shipping_traffic 17.97 1883919 9.537e-06 1
sewers_rain -66.53 2693857 -2.47e-05 1
sewers_waste 177 3450483 5.128e-05 1
wharves_city -232.7 12961639 -1.795e-05 1
wharves_industry 536.6 5483854 9.786e-05 0.9999
fisheries_trap -17.42 157690 -0.0001105 0.9999
fisheries_trawl -21.83 446444 -4.889e-05 1
fisheries_net 11.02 206739 5.329e-05 1
fisheries_dredge 204.7 653920 0.0003131 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 197 115 110 48.9 47 149 30.4 48.4 60.6 161 85.6 2.68 4.32 1.35 9.3

Hardametopa carinata

## SDM for: hardametopa_carinata

Abiotic parameters

## McFadden's pseudo-R2 is: -12.23 
## Tjur's pseudo-R2 is: -0.01 
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.301e+15 7978909 -413766906 0 * * *
om -1.493e+15 14257309 -104685824 0 * * *
gravel 7.237e+14 8714549 83047230 0 * * *
silt 1.384e+15 15274819 90612828 0 * * *
clay -7.527e+14 21858102 -34434528 0 * * *
arsenic -1.752e+15 11951982 -146562517 0 * * *
cadmium 6.267e+14 10400421 60260861 0 * * *
copper 1.939e+15 13687347 141670271 0 * * *
iron -4.869e+14 9620115 -50615564 0 * * *
manganese -5.417e+14 14288707 -37912203 0 * * *
mercury -6.123e+14 12068606 -50738739 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -108.2 230852 -0.0004686 0.9996
aquaculture -372.7 7074900 -5.267e-05 1
city -426.4 3308637 -0.0001289 0.9999
dredging_collect -235.4 21696254 -1.085e-05 1
dredging_dump -82.52 6321537 -1.305e-05 1
industry 213.3 1182221 0.0001805 0.9999
shipping_mooring -280.9 11462006 -2.451e-05 1
shipping_traffic -98.85 3244147 -3.047e-05 1
sewers_rain 103.2 6422833 1.607e-05 1
sewers_waste -34.09 8749498 -3.896e-06 1
wharves_city 620.2 4779428 0.0001298 0.9999
wharves_industry 91.44 28604720 3.197e-06 1
fisheries_trap -14.2 464038 -3.06e-05 1
fisheries_trawl 11.79 560768 2.102e-05 1
fisheries_net 5.979 272327 2.195e-05 1
fisheries_dredge 40.26 6081530 6.621e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 58.6 47.9 255 80.8 13 103 34.1 46.3 61.4 78.8 312 9.76 3.81 1.77 36.1

Harmothoe sp

## SDM for: harmothoe_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.31 
## Tjur's pseudo-R2 is: 0.15 
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -297.4 123467 -0.002409 0.9981
om -1.349 1.648 -0.8187 0.413
gravel 1.754 1.197 1.465 0.1428
silt 3.209 3.034 1.058 0.2901
clay -1593 673737 -0.002365 0.9981
arsenic -0.8131 1.986 -0.4093 0.6823
cadmium -0.8947 2.331 -0.3838 0.7011
copper 1.42 2.483 0.5717 0.5675
iron 0.08593 1.526 0.0563 0.9551
manganese 1.257 2.861 0.4393 0.6605
mercury -2.617 2.43 -1.077 0.2815 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.2 3.61 4.59 1 1.72 2.43 3.03 1.68 3.59 2.85

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -67.01 140671 -0.0004764 0.9996
aquaculture 71.4 4855881 1.47e-05 1
city 145.5 10430881 1.395e-05 1
dredging_collect 77.28 774915 9.973e-05 0.9999
dredging_dump -69.1 2160445 -3.199e-05 1
industry 46.68 2184647 2.137e-05 1
shipping_mooring 71.69 2732105 2.624e-05 1
shipping_traffic 44.99 2913228 1.544e-05 1
sewers_rain -7.218 4567222 -1.58e-06 1
sewers_waste 13.37 2628820 5.086e-06 1
wharves_city -175.3 10730018 -1.633e-05 1
wharves_industry -93.08 1858782 -5.008e-05 1
fisheries_trap 6.762 579912 1.166e-05 1
fisheries_trawl -4.493 150652 -2.982e-05 1
fisheries_net 8.237 405683 2.03e-05 1
fisheries_dredge -8.058 914003 -8.816e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 78.4 177 16.8 41 44.1 52.6 45.5 68 41.8 171 36.4 8.2 4.69 4.34 5.39

Harpacticoida

## SDM for: harpacticoida

Abiotic parameters

## McFadden's pseudo-R2 is: 0.15 
## Tjur's pseudo-R2 is: 0.18 
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.1307 0.279 -0.4684 0.6395
om -0.2423 0.4816 -0.5032 0.6148
gravel -0.4946 0.3036 -1.629 0.1033
silt -0.9537 0.5323 -1.792 0.07321
clay 0.01964 0.6813 0.02883 0.977
arsenic -0.6126 0.6537 -0.9372 0.3487
cadmium 0.09313 0.3354 0.2777 0.7813
copper 0.2464 0.4647 0.5302 0.5959
iron -0.007579 0.2988 -0.02537 0.9798
manganese -0.2256 0.483 -0.467 0.6405
mercury 0.9802 0.4596 2.133 0.03295 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.86 1.39 2.13 1.1 1.83 1.45 1.93 1.37 1.81 1.72

Influence indices

## McFadden's pseudo-R2 is: 0.33 
## Tjur's pseudo-R2 is: 0.39 
## Pearson's pseudo-R2 is: 0.38
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -60.41 7297 -0.008279 0.9934
aquaculture -2.306 2.527 -0.9127 0.3614
city -1.173 2.369 -0.4949 0.6206
dredging_collect 5.026 2.276 2.208 0.02723 *
dredging_dump -0.5171 2.027 -0.2551 0.7987
industry 0.4183 1.09 0.3838 0.7012
shipping_mooring -0.4469 1.866 -0.2394 0.8108
shipping_traffic 1.482 0.9529 1.555 0.1199
sewers_rain -1.321 2.45 -0.5392 0.5897
sewers_waste 0.6535 3.281 0.1992 0.8421
wharves_city 2.4 2.821 0.8509 0.3948
wharves_industry -6.804 3.41 -1.995 0.04602 *
fisheries_trap 0.1792 0.4054 0.4421 0.6585
fisheries_trawl 0.0565 0.3018 0.1872 0.8515
fisheries_net -625.7 75659 -0.008271 0.9934
fisheries_dredge -0.06066 1.075 -0.05643 0.955 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 8.6 8.78 7.88 7.39 4.23 5.88 3.44 6.93 9.95 10.6 12.1 1.05 1.39 1 1.44

Hediste diversicolor

## SDM for: hediste_diversicolor

Abiotic parameters

## McFadden's pseudo-R2 is: 0.41 
## Tjur's pseudo-R2 is: 0.38 
## Pearson's pseudo-R2 is: 0.37
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.26 1.173 -3.632 0.0002807 * * *
om 0.5063 0.8401 0.6027 0.5467
gravel -0.2704 0.5688 -0.4753 0.6346
silt -0.1369 0.9696 -0.1412 0.8877
clay 1.074 0.8815 1.218 0.2232
arsenic -6.008 2.312 -2.598 0.009373 * *
cadmium 1.08 0.6571 1.643 0.1004
copper 2.731 1.241 2.2 0.02783 *
iron -5.956 2.654 -2.245 0.0248 *
manganese 4.928 2.201 2.239 0.02515 *
mercury -1.069 1.147 -0.9322 0.3512 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.24 1.81 2.25 1.36 2.7 1.4 2.69 5.01 5.91 2.62

Influence indices

## McFadden's pseudo-R2 is: -9.37 
## Tjur's pseudo-R2 is: 0.52 
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.496e+15 7496963 -199610397 0 * * *
aquaculture -2.707e+14 66771996 -4053672 0 * * *
city 6.524e+14 60515998 10779871 0 * * *
dredging_collect 2.138e+15 47886179 44656506 0 * * *
dredging_dump 2.622e+15 55589796 47167271 0 * * *
industry -6.664e+14 30193191 -22072515 0 * * *
shipping_mooring -2.245e+14 51224704 -4383618 0 * * *
shipping_traffic -5.961e+14 22815885 -26125201 0 * * *
sewers_rain 1.942e+15 67164046 28908808 0 * * *
sewers_waste -1.699e+15 90359677 -18805353 0 * * *
wharves_city -1.222e+15 72465526 -16857801 0 * * *
wharves_industry -3.239e+15 79067645 -40960911 0 * * *
fisheries_trap -3.874e+13 7277539 -5323487 0 * * *
fisheries_trawl -9.891e+13 8821984 -11211621 0 * * *
fisheries_net -3.76e+14 7219163 -52088433 0 * * *
fisheries_dredge -6.069e+14 19454028 -31194212 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Heteranomia squamula

## SDM for: heteranomia_squamula

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -95.8 274909 -0.0003485 0.9997
om -10.42 524836 -1.985e-05 1
gravel 6.602 77488 8.52e-05 0.9999
silt -1.753 229514 -7.637e-06 1
clay 23.84 1153878 2.066e-05 1
arsenic -107.1 345443 -0.0003101 0.9998
cadmium -11.83 187356 -6.316e-05 0.9999
copper 7.065 286431 2.467e-05 1
iron -26.82 273093 -9.822e-05 0.9999
manganese 42.82 545233 7.854e-05 0.9999
mercury 10.22 290476 3.52e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.62 2.62 3.86 2 1.76 5.63 3.51 3.92 8.2 3.55

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -116.6 240165 -0.0004857 0.9996
aquaculture -168.3 1102516 -0.0001526 0.9999
city 251.4 1289120 0.000195 0.9998
dredging_collect 9.64 1661898 5.801e-06 1
dredging_dump 151.3 1022875 0.000148 0.9999
industry -34.59 566055 -6.111e-05 1
shipping_mooring 2.434 869643 2.799e-06 1
shipping_traffic -48.51 852461 -5.69e-05 1
sewers_rain 332.8 1136950 0.0002927 0.9998
sewers_waste -562 1673553 -0.0003358 0.9997
wharves_city -312.7 1361612 -0.0002297 0.9998
wharves_industry 27.32 2040076 1.339e-05 1
fisheries_trap 18.7 82254 0.0002273 0.9998
fisheries_trawl 16.11 167002 9.649e-05 0.9999
fisheries_net 3.69 162216 2.275e-05 1
fisheries_dredge 30.81 692729 4.448e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 12.6 20.1 19 12.9 6.88 7.74 11 11.3 15.7 23.9 24.1 2.03 2 1.04 6.34

Hiatella arctica

## SDM for: hiatella_arctica

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2069 2995166 -0.0006908 0.9994
om -880.4 1369615 -0.0006428 0.9995
gravel 66.57 119194 0.0005585 0.9996
silt 222.7 384679 0.000579 0.9995
clay 410.3 622924 0.0006587 0.9995
arsenic -1433 2195989 -0.0006526 0.9995
cadmium -57.33 194404 -0.0002949 0.9998
copper 318.8 534368 0.0005965 0.9995
iron 113.7 342324 0.0003321 0.9997
manganese -701.2 1328934 -0.0005276 0.9996
mercury -355.2 613684 -0.0005788 0.9995 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 10.3 4.01 8.62 5.51 7.55 2.85 6.95 4.61 6.53 6.88

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -443.8 1029448 -0.0004311 0.9997
aquaculture -600.8 2609621 -0.0002302 0.9998
city 812.1 2162626 0.0003755 0.9997
dredging_collect -82.7 4328889 -1.91e-05 1
dredging_dump 793.4 2288047 0.0003468 0.9997
industry -281.4 1022890 -0.0002751 0.9998
shipping_mooring -67.99 799813 -8.501e-05 0.9999
shipping_traffic -94.08 612474 -0.0001536 0.9999
sewers_rain 1398 3391622 0.0004122 0.9997
sewers_waste -2405 5935618 -0.0004052 0.9997
wharves_city -1166 3147721 -0.0003703 0.9997
wharves_industry -4.435 4562613 -9.72e-07 1
fisheries_trap 12.56 204042 6.154e-05 1
fisheries_trawl -248.2 1438760 -0.0001725 0.9999
fisheries_net -6.259 6453060 -9.7e-07 1
fisheries_dredge -79.1 1875302 -4.218e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 58.2 33.1 66.9 36.5 14.6 15.4 13.2 67.5 100 50.5 74.1 2.22 1.61 1.02 17.6

Holothuroidea

## SDM for: holothuroidea

Abiotic parameters

## McFadden's pseudo-R2 is: 0.65 
## Tjur's pseudo-R2 is: 0.52 
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -41.99 38706 -0.001085 0.9991
om 2.056 4.859 0.4231 0.6722
gravel -30.85 29306 -0.001053 0.9992
silt -4.327 4.403 -0.9826 0.3258
clay -135.6 206676 -0.0006559 0.9995
arsenic -1.495 9.051 -0.1652 0.8688
cadmium -1.857 3.493 -0.5317 0.5949
copper 3.585 3.232 1.109 0.2674
iron -1.275 2.689 -0.4743 0.6353
manganese -2.835 7.675 -0.3694 0.7119
mercury -0.6491 4.929 -0.1317 0.8952 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.41 1 2.44 1 1.83 2.46 2.64 2.12 2.41 2.37

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -117.5 231099 -0.0005084 0.9996
aquaculture -109.7 2079988 -5.276e-05 1
city 176 1291820 0.0001362 0.9999
dredging_collect 157.6 914144 0.0001724 0.9999
dredging_dump -245.8 1839023 -0.0001337 0.9999
industry 37.95 852624 4.451e-05 1
shipping_mooring -3.61 1441338 -2.504e-06 1
shipping_traffic 129 634858 0.0002032 0.9998
sewers_rain -84.31 1654545 -5.096e-05 1
sewers_waste 26 2349337 1.107e-05 1
wharves_city -95.41 1549425 -6.157e-05 1
wharves_industry -4.361 1951443 -2.235e-06 1
fisheries_trap 14.51 160817 9.021e-05 0.9999
fisheries_trawl 21.86 161670 0.0001352 0.9999
fisheries_net 11.04 167222 6.6e-05 0.9999
fisheries_dredge 49.21 159347 0.0003088 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 22.4 23.1 11.3 32.8 10.1 22.4 13.7 30.3 33.6 28.8 35.8 3.03 1.87 1.08 4.13

Idotea phosphorea

## SDM for: idotea_phosphorea

Abiotic parameters

## McFadden's pseudo-R2 is: 0.42 
## Tjur's pseudo-R2 is: 0.23 
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.73 5.744 -1.867 0.06186
om -4.45 2.928 -1.52 0.1285
gravel 1.461 1.319 1.107 0.2681
silt 2.092 1.525 1.372 0.1701
clay -2.414 7.27 -0.332 0.7399
arsenic -0.5591 4.273 -0.1308 0.8959
cadmium 4.091 2.911 1.406 0.1599
copper 5.016 3.68 1.363 0.1729
iron -7.252 6.092 -1.19 0.2339
manganese -3.492 5.555 -0.6286 0.5296
mercury -2.26 2.601 -0.8692 0.3847 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.12 1.91 2.06 1 2.46 3.82 3.43 4.39 3.15 1.18

Influence indices

## McFadden's pseudo-R2 is: -12.85 
## Tjur's pseudo-R2 is: -0.02 
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.572e+15 7496963 -3.43e+08 0 * * *
aquaculture 1.796e+15 66771996 26895007 0 * * *
city -4.621e+13 60515998 -763659 0 * * *
dredging_collect 1.622e+15 47886179 33882278 0 * * *
dredging_dump 3.937e+15 55589796 70828526 0 * * *
industry -2.988e+15 30193191 -98976231 0 * * *
shipping_mooring 1.311e+15 51224704 25595241 0 * * *
shipping_traffic 3.257e+14 22815885 14276787 0 * * *
sewers_rain 2.636e+15 67164046 39249060 0 * * *
sewers_waste -3.848e+15 90359677 -42583707 0 * * *
wharves_city -1.15e+15 72465526 -15874733 0 * * *
wharves_industry -3.483e+15 79067645 -44050203 0 * * *
fisheries_trap -1.856e+14 7277539 -25500734 0 * * *
fisheries_trawl -6.092e+14 8821984 -69059358 0 * * *
fisheries_net -4.158e+14 7219163 -57597454 0 * * *
fisheries_dredge -5.693e+14 19454028 -29265673 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Ischyroceridae spp

## SDM for: ischyroceridae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -164.2 390466 -0.0004205 0.9997
om 123.5 326201 0.0003785 0.9997
gravel -37.77 359356 -0.0001051 0.9999
silt -77.04 770127 -1e-04 0.9999
clay 98.65 686832 0.0001436 0.9999
arsenic -142.2 711751 -0.0001998 0.9998
cadmium -27.17 605434 -4.488e-05 1
copper 97.63 388985 0.000251 0.9998
iron -116.7 737376 -0.0001583 0.9999
manganese 37.35 552277 6.762e-05 0.9999
mercury 30.18 251967 0.0001198 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.82 2.89 9.81 1.1 5.57 9.06 5.56 8.88 6.16 3.33

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -90.81 346796 -0.0002618 0.9998
aquaculture 337.7 14867673 2.271e-05 1
city 74.87 20706774 3.616e-06 1
dredging_collect 226.6 6417189 3.531e-05 1
dredging_dump 56.87 5003265 1.137e-05 1
industry -175.2 1904624 -9.2e-05 0.9999
shipping_mooring 293.7 8995786 3.265e-05 1
shipping_traffic 66.97 5979316 1.12e-05 1
sewers_rain -15.88 2149956 -7.387e-06 1
sewers_waste 25.65 7702137 3.331e-06 1
wharves_city -192.5 20119042 -9.568e-06 1
wharves_industry -235.9 2676196 -8.815e-05 0.9999
fisheries_trap -7.447 345469 -2.156e-05 1
fisheries_trawl -15.2 1249359 -1.216e-05 1
fisheries_net -3.215 223978 -1.435e-05 1
fisheries_dredge 1.873 1597431 1.173e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 125 305 57.9 55.2 10.8 85.1 55.5 18.4 63.7 330 24.6 2.21 12.2 1.46 9.59

Ischyrocerus anguipes

## SDM for: ischyrocerus_anguipes

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -319.1 634744 -0.0005027 0.9996
om 144.7 353727 0.0004092 0.9997
gravel -32.85 217729 -0.0001509 0.9999
silt -158.5 343731 -0.0004611 0.9996
clay 125.3 1084890 0.0001155 0.9999
arsenic -184.5 436052 -0.0004231 0.9997
cadmium 223.3 455413 0.0004903 0.9996
copper 273.8 533741 0.0005131 0.9996
iron -342.8 882909 -0.0003883 0.9997
manganese 38.51 562287 6.849e-05 0.9999
mercury -6.083 134269 -4.531e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.27 2.29 3.06 1.03 2.44 6.49 7.75 5.88 4.12 1.71

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -168.3 395546 -0.0004254 0.9997
aquaculture -234.5 1467949 -0.0001597 0.9999
city -220.3 1125202 -0.0001958 0.9998
dredging_collect -144.8 1151325 -0.0001257 0.9999
dredging_dump 53.78 1473918 3.649e-05 1
industry 20.69 740181 2.796e-05 1
shipping_mooring -276.2 947571 -0.0002915 0.9998
shipping_traffic -75.77 3504485 -2.162e-05 1
sewers_rain -170.3 2071718 -8.22e-05 0.9999
sewers_waste 148.1 2867421 5.166e-05 1
wharves_city 347.5 1350164 0.0002574 0.9998
wharves_industry 122.7 4656250 2.635e-05 1
fisheries_trap -212.6 608992 -0.0003491 0.9997
fisheries_trawl 18.76 4028777 4.655e-06 1
fisheries_net 32.87 477089 6.891e-05 0.9999
fisheries_dredge -27.6 456481 -6.046e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 12.8 15 17 25.2 11.5 9.14 54.1 17.4 22.8 22.2 69.1 1.7 28.3 1.89 1.85

Isopoda

## SDM for: isopoda

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Lacuna vincta

## SDM for: lacuna_vincta

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -511.8 862740 -0.0005932 0.9995
om -261.7 454872 -0.0005754 0.9995
gravel 86.71 157831 0.0005494 0.9996
silt 75.79 203871 0.0003718 0.9997
clay 43.83 271758 0.0001613 0.9999
arsenic 54.42 2718342 2.002e-05 1
cadmium 229.9 761955 0.0003017 0.9998
copper 104.5 342807 0.0003048 0.9998
iron -256.6 4070557 -6.304e-05 0.9999
manganese -191.8 3350724 -5.723e-05 1
mercury -14.6 1024508 -1.425e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.79 3.93 3.6 1.1 13.2 11 4.14 31.3 26.9 6.64

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -239.4 988003 -0.0002423 0.9998
aquaculture 1113 17795634 6.255e-05 1
city 385.5 2236045 0.0001724 0.9999
dredging_collect 133.7 12374406 1.08e-05 1
dredging_dump -81.54 3307523 -2.465e-05 1
industry -464.9 2014431 -0.0002308 0.9998
shipping_mooring 701.1 3892682 0.0001801 0.9999
shipping_traffic 186.5 2400153 7.772e-05 0.9999
sewers_rain -409.1 16316413 -2.507e-05 1
sewers_waste 636.8 21629851 2.944e-05 1
wharves_city -596.9 3843564 -0.0001553 0.9999
wharves_industry 15.41 12632497 1.22e-06 1
fisheries_trap 32.12 262169 0.0001225 0.9999
fisheries_trawl -42.42 1651366 -2.569e-05 1
fisheries_net 4.778 578096 8.264e-06 1
fisheries_dredge -125.8 15207593 -8.275e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 231 55.9 212 39.3 35.2 45.5 49.4 181 185 69.6 223 7.63 9.47 2.29 79.5

Lamprops fuscatus

## SDM for: lamprops_fuscatus

Abiotic parameters

## McFadden's pseudo-R2 is: 0.19 
## Tjur's pseudo-R2 is: 0.16 
## Pearson's pseudo-R2 is: 0.15
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.959 0.7149 -4.138 3.498e-05 * * *
om -0.988 0.8626 -1.145 0.2521
gravel -0.5766 0.8832 -0.6528 0.5139
silt 0.159 0.7361 0.2159 0.829
clay -0.5512 1.471 -0.3747 0.7079
arsenic -2.131 1.523 -1.399 0.1617
cadmium -0.3191 0.5505 -0.5796 0.5622
copper 0.6262 0.6349 0.9863 0.324
iron 0.4182 0.4128 1.013 0.3111
manganese 0.3923 0.9809 0.3999 0.6892
mercury 0.04733 0.7228 0.06548 0.9478 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.52 1.12 1.66 1.05 1.6 1.42 1.48 1.46 1.99 1.58

Influence indices

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.29 
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -66.44 9700 -0.006849 0.9945
aquaculture 11.44 9.114 1.255 0.2095
city 9.346 7.397 1.264 0.2064
dredging_collect 3.002 3.887 0.7722 0.44
dredging_dump -6.606 4.142 -1.595 0.1107
industry -1.924 1.846 -1.042 0.2973
shipping_mooring 8.09 6.575 1.23 0.2185
shipping_traffic 3.399 2.441 1.392 0.1639
sewers_rain -5.321 4.111 -1.295 0.1955
sewers_waste 6.127 6.398 0.9577 0.3382
wharves_city -10.15 8.193 -1.239 0.2155
wharves_industry 2.932 4.124 0.711 0.4771
fisheries_trap 0.577 0.3787 1.523 0.1277
fisheries_trawl -7.864 11.24 -0.6995 0.4842
fisheries_net -638.6 100581 -0.006349 0.9949
fisheries_dredge 1.405 1.029 1.366 0.1719 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 21.2 19.1 9.12 10.4 3.18 16.9 6.7 10.7 16.2 22.4 10.1 1.32 1.75 1 2.28

Lamprops quadriplicata

## SDM for: lamprops_quadriplicata

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -127.6 231428 -0.0005514 0.9996
om -21.63 338176 -6.396e-05 0.9999
gravel -1.938 252060 -7.69e-06 1
silt -38.07 163499 -0.0002329 0.9998
clay 45.35 409897 0.0001106 0.9999
arsenic 59.89 181479 0.00033 0.9997
cadmium 1.642 115825 1.418e-05 1
copper -33.93 242411 -0.00014 0.9999
iron -136.1 451420 -0.0003014 0.9998
manganese 71.43 337247 0.0002118 0.9998
mercury 35.88 253134 0.0001417 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.05 2.18 3.68 1.11 2.02 1.87 3.79 4.07 3.98 2.65

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -223.8 399631 -0.0005599 0.9996
aquaculture 680.6 1783582 0.0003816 0.9997
city -497.6 1488129 -0.0003344 0.9997
dredging_collect 405.2 2185261 0.0001854 0.9999
dredging_dump 244.8 717180 0.0003414 0.9997
industry -125.7 915888 -0.0001373 0.9999
shipping_mooring 524.9 1853575 0.0002832 0.9998
shipping_traffic -257.8 1768738 -0.0001457 0.9999
sewers_rain 99.46 3236096 3.073e-05 1
sewers_waste 419.6 4632917 9.057e-05 0.9999
wharves_city 195.7 1550384 0.0001262 0.9999
wharves_industry -684.3 1965543 -0.0003481 0.9997
fisheries_trap -38.66 988141 -3.912e-05 1
fisheries_trawl 31.7 193204 0.0001641 0.9999
fisheries_net -2.812 262612 -1.071e-05 1
fisheries_dredge 10.72 355324 3.018e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 17.8 31.7 48.6 15.1 26.9 21 44.4 46 59.9 23.5 49.8 2.78 2.2 1.04 2.47

Lepeta caeca

## SDM for: lepeta_caeca

Abiotic parameters

## McFadden's pseudo-R2 is: 0.39 
## Tjur's pseudo-R2 is: 0.25 
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -410.2 85646 -0.004789 0.9962
om -3.948 2.047 -1.929 0.05375
gravel 0.9993 0.5546 1.802 0.07155
silt 1.932 1.21 1.597 0.1102
clay -2204 467358 -0.004715 0.9962
arsenic -1.599 3.016 -0.5301 0.596
cadmium 0.9393 1.29 0.7282 0.4665
copper 2.132 1.63 1.307 0.1911
iron -0.08409 2.052 -0.04097 0.9673
manganese -2.695 2.961 -0.9101 0.3628
mercury -2.088 1.783 -1.171 0.2416 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.73 1.61 2.17 1 1.41 1.82 2.17 2.2 2.19 1.66

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -887.9 1856751 -0.0004782 0.9996
aquaculture -430 2642672 -0.0001627 0.9999
city 281.9 3659010 7.703e-05 0.9999
dredging_collect 176.4 1372489 0.0001285 0.9999
dredging_dump -865.6 4984482 -0.0001736 0.9999
industry 61.96 601524 0.000103 0.9999
shipping_mooring 99.58 1389029 7.169e-05 0.9999
shipping_traffic 101.1 1117764 9.047e-05 0.9999
sewers_rain 1038 3732573 0.0002781 0.9998
sewers_waste -2007 5354800 -0.0003749 0.9997
wharves_city 525.3 6434047 8.165e-05 0.9999
wharves_industry -137.2 2431662 -5.643e-05 1
fisheries_trap -46.48 307919 -0.0001509 0.9999
fisheries_trawl -10.23 72445 -0.0001412 0.9999
fisheries_net 22.84 6459583 3.536e-06 1
fisheries_dredge 15.01 196423 7.642e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 50.7 83.2 36.1 117 11.7 29.1 26.9 79.7 93.1 147 60.9 3.68 3.23 1 3.87

Leucon leucon nasicoides

## SDM for: leucon_leucon_nasicoides

Abiotic parameters

## McFadden's pseudo-R2 is: 0.28 
## Tjur's pseudo-R2 is: 0.32 
## Pearson's pseudo-R2 is: 0.32
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -320.2 33855 -0.009459 0.9925
om 1.615 0.603 2.678 0.007413 * *
gravel -0.4022 0.3651 -1.102 0.2706
silt -0.3783 0.5619 -0.6733 0.5007
clay -1745 184739 -0.009444 0.9925
arsenic 0.3244 0.4388 0.7392 0.4598
cadmium -0.5808 0.4745 -1.224 0.2209
copper -0.6626 0.5433 -1.22 0.2226
iron -0.3635 0.3658 -0.9938 0.3203
manganese 0.2702 0.608 0.4444 0.6568
mercury 0.6638 0.4925 1.348 0.1777 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.34 1.98 1 1.49 1.67 1.99 1.43 1.87 1.67

Influence indices

## McFadden's pseudo-R2 is: 0.4 
## Tjur's pseudo-R2 is: 0.43 
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.466 1.846 -2.419 0.01556 *
aquaculture 6.603 3.41 1.936 0.05285
city -22.52 12.87 -1.749 0.08027
dredging_collect 7.098 3.152 2.252 0.02434 *
dredging_dump -1.318 3.524 -0.3741 0.7083
industry -2.258 1.541 -1.465 0.1429
shipping_mooring 18.82 6.696 2.811 0.004939 * *
shipping_traffic 2.445 1.608 1.521 0.1283
sewers_rain 4.06 3.239 1.254 0.21
sewers_waste -13.73 5.982 -2.294 0.02177 *
wharves_city 12.93 10.36 1.248 0.2119
wharves_industry -4.411 4.559 -0.9675 0.3333
fisheries_trap -2.454 1.655 -1.483 0.138
fisheries_trawl -0.05317 0.343 -0.155 0.8768
fisheries_net 0.487 2.51 0.194 0.8462
fisheries_dredge 0.6187 0.6818 0.9075 0.3642 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 13.1 33.9 9.28 10.7 3.3 24.9 5.06 11.3 22 29.3 13.6 1.19 1.46 1 1.67

Littorina littorea

## SDM for: littorina_littorea

Abiotic parameters

## McFadden's pseudo-R2 is: 0.52 
## Tjur's pseudo-R2 is: 0.43 
## Pearson's pseudo-R2 is: 0.47
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -382.7 89075 -0.004296 0.9966
om -2.203 2.959 -0.7445 0.4566
gravel 1.003 1.336 0.7509 0.4527
silt 1.443 1.581 0.9125 0.3615
clay -2034 486068 -0.004185 0.9967
arsenic -1.353 3.2 -0.4227 0.6725
cadmium 4.213 3.224 1.307 0.1913
copper 5.345 4.058 1.317 0.1878
iron -5.683 5.524 -1.029 0.3036
manganese -5.868 5.174 -1.134 0.2567
mercury -2.599 3.533 -0.7357 0.4619 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.62 1.6 1.69 1 2.64 4.63 5.49 5.65 2.98 1.75

Influence indices

## McFadden's pseudo-R2 is: -7.31 
## Tjur's pseudo-R2 is: 0.64 
## Pearson's pseudo-R2 is: 0.31
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.241e+15 7496963 -298928083 0 * * *
aquaculture 6.476e+14 66771996 9699157 0 * * *
city 4.011e+15 60515998 66271739 0 * * *
dredging_collect 1.567e+15 47886179 32718840 0 * * *
dredging_dump 3.17e+14 55589796 5702620 0 * * *
industry -9.946e+14 30193191 -32940931 0 * * *
shipping_mooring 1.77e+14 51224704 3455393 0 * * *
shipping_traffic 1.316e+15 22815885 57668144 0 * * *
sewers_rain -6.65e+14 67164046 -9900944 0 * * *
sewers_waste -4.773e+14 90359677 -5281913 0 * * *
wharves_city -4.452e+15 72465526 -61433156 0 * * *
wharves_industry -1.523e+15 79067645 -19261641 0 * * *
fisheries_trap -3.431e+14 7277539 -47139753 0 * * *
fisheries_trawl -6.619e+14 8821984 -75027511 0 * * *
fisheries_net -2.349e+14 7219163 -32537781 0 * * *
fisheries_dredge -1.69e+15 19454028 -86868875 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Lumbrineridae spp

## SDM for: lumbrineridae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.73 
## Tjur's pseudo-R2 is: 0.58 
## Pearson's pseudo-R2 is: 0.55
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -35.04 3717 -0.009427 0.9925
om 5.15 7.01 0.7347 0.4625
gravel -1.202 3486 -0.0003448 0.9997
silt 10.18 14.35 0.7093 0.4781
clay -39.14 20011 -0.001956 0.9984
arsenic -28.34 41.06 -0.6901 0.4902
cadmium 4.427 4.451 0.9944 0.32
copper -1.98 3.299 -0.6004 0.5483
iron 2.055 3.913 0.5253 0.5994
manganese -1.216 6.657 -0.1827 0.855
mercury -6.542 8.093 -0.8083 0.4189 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.65 1 5.12 1 6.19 3.84 1.41 2.35 2.39 2.49

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -147.9 370319 -0.0003995 0.9997
aquaculture -193.7 2871761 -6.746e-05 0.9999
city 16.17 2532646 6.384e-06 1
dredging_collect -11.86 3264151 -3.633e-06 1
dredging_dump 417.1 2883451 0.0001447 0.9999
industry 98.7 740348 0.0001333 0.9999
shipping_mooring -52.27 2146387 -2.435e-05 1
shipping_traffic -216.2 554826 -0.0003896 0.9997
sewers_rain 528.7 2288835 0.000231 0.9998
sewers_waste -618.8 3338517 -0.0001853 0.9999
wharves_city -170.9 2584897 -6.611e-05 0.9999
wharves_industry -230.1 5547876 -4.148e-05 1
fisheries_trap 30.32 293547 0.0001033 0.9999
fisheries_trawl 13.37 116131 0.0001151 0.9999
fisheries_net -0.12 160878 -7.462e-07 1
fisheries_dredge -98.36 644362 -0.0001527 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 83 41.9 66.4 45.9 8.03 47.8 11 53.1 92 47.7 104 10.3 3.26 1.05 9.06

Lysianassidae spp

## SDM for: lysianassidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -349.9 719518 -0.0004862 0.9996
om -96.82 242602 -0.0003991 0.9997
gravel -249.9 767392 -0.0003256 0.9997
silt 101 261474 0.0003864 0.9997
clay 27.24 133190 0.0002045 0.9998
arsenic -249.2 932312 -0.0002673 0.9998
cadmium -6.558 187757 -3.493e-05 1
copper 52.95 379490 0.0001395 0.9999
iron -17.1 597125 -2.864e-05 1
manganese 12.54 687137 1.825e-05 1
mercury -129.1 278648 -0.0004635 0.9996 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.57 1.71 3.13 1.49 2.7 2.7 4.5 6 4.35 2.82

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -115.8 463451 -0.0002498 0.9998
aquaculture 233.6 8135597 2.871e-05 1
city 349 6258054 5.576e-05 1
dredging_collect 48.91 2745551 1.781e-05 1
dredging_dump -280.4 5399354 -5.194e-05 1
industry -53.64 1318456 -4.069e-05 1
shipping_mooring 127.2 6420447 1.981e-05 1
shipping_traffic 81.35 3391907 2.398e-05 1
sewers_rain -163.6 3652552 -4.478e-05 1
sewers_waste 145.7 7481054 1.948e-05 1
wharves_city -359 6124051 -5.863e-05 1
wharves_industry 251.4 2131870 0.0001179 0.9999
fisheries_trap 14.15 185408 7.631e-05 0.9999
fisheries_trawl 2.76 112433 2.454e-05 1
fisheries_net 10.57 186491 5.668e-05 1
fisheries_dredge 20.01 3021091 6.625e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 93.6 111 52.4 106 20 76.6 66.4 48.5 84.9 118 41.6 3.03 1.49 1.22 30.8

Macoma calcarea

## SDM for: macoma_calcarea

Abiotic parameters

## McFadden's pseudo-R2 is: 0.16 
## Tjur's pseudo-R2 is: 0.17 
## Pearson's pseudo-R2 is: 0.17
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.643 0.3659 4.489 7.142e-06 * * *
om 1.271 0.6414 1.982 0.04749 *
gravel -0.4591 0.3209 -1.431 0.1525
silt -0.187 0.6078 -0.3077 0.7583
clay -0.3593 0.708 -0.5075 0.6118
arsenic -0.06127 0.4142 -0.1479 0.8824
cadmium -0.4431 0.4017 -1.103 0.2701
copper -0.7986 0.5216 -1.531 0.1258
iron -0.3209 0.4634 -0.6923 0.4887
manganese 0.4665 0.5369 0.8688 0.3849
mercury -0.2265 0.4687 -0.4833 0.6289 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.03 1.52 2.08 1.11 1.48 1.37 1.89 1.49 1.74 1.56

Influence indices

## McFadden's pseudo-R2 is: 0.21 
## Tjur's pseudo-R2 is: 0.24 
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.243 0.6817 3.29 0.001002 * *
aquaculture -3.01 3.444 -0.8739 0.3822
city 1.137 2.565 0.4432 0.6576
dredging_collect 7.379 4.569 1.615 0.1063
dredging_dump 3.446 2.272 1.517 0.1293
industry 1.773 1.701 1.042 0.2973
shipping_mooring 0.2284 2.123 0.1076 0.9143
shipping_traffic 0.5625 1.009 0.5574 0.5772
sewers_rain 2.783 2.692 1.034 0.3012
sewers_waste -5.062 3.774 -1.341 0.1798
wharves_city -1.454 3.087 -0.4711 0.6376
wharves_industry -11.4 5.684 -2.005 0.04493 *
fisheries_trap -0.2775 0.2978 -0.9318 0.3515
fisheries_trawl 0.2448 0.4544 0.5388 0.59
fisheries_net 0.5933 2.91 0.2039 0.8384
fisheries_dredge 1.712 1.865 0.918 0.3586 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.6 8.79 15.6 7.88 6.37 6.7 3.61 8.37 11.8 10 19.8 1.3 1.57 1 2.79

Maera danae

## SDM for: maera_danae

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -645.8 3768764 -0.0001714 0.9999
om -266.9 611132 -0.0004367 0.9997
gravel -134.3 743421 -0.0001806 0.9999
silt 19.72 357831 5.512e-05 1
clay 1.933 19767865 9.78e-08 1
arsenic -116.4 470332 -0.0002475 0.9998
cadmium 302.8 679239 0.0004459 0.9996
copper 422 875479 0.000482 0.9996
iron -592.8 1397448 -0.0004242 0.9997
manganese -137.3 757012 -0.0001814 0.9999
mercury 60.15 198975 0.0003023 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 18.7 1.36 5.04 1.12 8.45 18.9 29 29.7 2.96 2.61

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -71.01 140791 -0.0005043 0.9996
aquaculture 1.465 1423813 1.029e-06 1
city -161.8 1043763 -0.000155 0.9999
dredging_collect 3.309 1272201 2.601e-06 1
dredging_dump -26.73 1302138 -2.053e-05 1
industry 74.63 725722 0.0001028 0.9999
shipping_mooring -18.84 2029726 -9.282e-06 1
shipping_traffic -73.09 1305542 -5.599e-05 1
sewers_rain 30 1337511 2.243e-05 1
sewers_waste 84.55 1870889 4.519e-05 1
wharves_city 187 1966847 9.506e-05 0.9999
wharves_industry -75.07 1924373 -3.901e-05 1
fisheries_trap 7.911 79350 9.97e-05 0.9999
fisheries_trawl 4.787 214578 2.231e-05 1
fisheries_net 1.25 121769 1.026e-05 1
fisheries_dredge 0.9453 383211 2.467e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 18.6 22.8 26.7 27.9 15 24.3 26.1 16 21.8 48.2 38.9 5.76 3.25 1.3 2.72

Maldane sarsi

## SDM for: maldane_sarsi

Abiotic parameters

## McFadden's pseudo-R2 is: -6.59 
## Tjur's pseudo-R2 is: 0 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.92e+15 7978909 -240653949 0 * * *
om -2.389e+14 14257309 -16759234 0 * * *
gravel -2.197e+13 8714549 -2521177 0 * * *
silt 9.546e+14 15274819 62495942 0 * * *
clay -1.683e+15 21858102 -76993339 0 * * *
arsenic -2.395e+14 11951982 -20042071 0 * * *
cadmium -7.473e+14 10400421 -71850925 0 * * *
copper 1.349e+15 13687347 98584389 0 * * *
iron -5.594e+14 9620115 -58152424 0 * * *
manganese 3.474e+12 14288707 243142 0 * * *
mercury -4.07e+14 12068606 -33720427 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -81.54 137742 -0.000592 0.9995
aquaculture -54.11 2385465 -2.269e-05 1
city -105.3 677852 -0.0001554 0.9999
dredging_collect -329.6 844043 -0.0003905 0.9997
dredging_dump -3.8 1380487 -2.752e-06 1
industry 25.97 534623 4.858e-05 1
shipping_mooring -122.6 1270886 -9.646e-05 0.9999
shipping_traffic -35.53 297053 -0.0001196 0.9999
sewers_rain 0.266 1531880 1.736e-07 1
sewers_waste 85.02 2227575 3.817e-05 1
wharves_city 177.3 996571 0.0001779 0.9999
wharves_industry 299.4 1342407 0.000223 0.9998
fisheries_trap -3.824 167868 -2.278e-05 1
fisheries_trawl -11.76 619325 -1.898e-05 1
fisheries_net 7.32 101990 7.178e-05 0.9999
fisheries_dredge 1.246 1136493 1.096e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 40.4 10.7 17.3 25.5 11.4 20.8 6.33 34.1 49.5 15.7 28.5 1.4 5.82 1.09 8.91

Maldanidae spp

## SDM for: maldanidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.18 
## Tjur's pseudo-R2 is: 0.16 
## Pearson's pseudo-R2 is: 0.15
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -9.236 736.6 -0.01254 0.99
om 0.2894 0.5228 0.5536 0.5799
gravel -28.91 2630 -0.01099 0.9912
silt 0.3825 0.6283 0.6088 0.5427
clay -0.8682 1.489 -0.5831 0.5598
arsenic -0.2267 0.4389 -0.5166 0.6055
cadmium -0.5311 0.5208 -1.02 0.3078
copper 0.4499 0.5702 0.7891 0.4301
iron -0.2214 0.4848 -0.4566 0.6479
manganese -0.2024 0.5672 -0.3568 0.7212
mercury 0.4984 0.4506 1.106 0.2687 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.88 1 1.97 1.03 1.57 1.74 2.07 1.58 1.9 1.55

Influence indices

## McFadden's pseudo-R2 is: 0.32 
## Tjur's pseudo-R2 is: 0.34 
## Pearson's pseudo-R2 is: 0.33
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -77.43 8287 -0.009343 0.9925
aquaculture 4.546 2.928 1.553 0.1205
city -19.81 10.29 -1.925 0.05422
dredging_collect 4.522 2.524 1.791 0.07323
dredging_dump -2.662 2.844 -0.936 0.3493
industry -0.7146 1.253 -0.5701 0.5686
shipping_mooring 11.43 4.166 2.744 0.006075 * *
shipping_traffic 0.6896 1.244 0.5541 0.5795
sewers_rain 1.686 3.786 0.4452 0.6562
sewers_waste -4.832 5.353 -0.9027 0.3667
wharves_city 15.34 8.609 1.782 0.07482
wharves_industry -3.486 4.252 -0.82 0.4122
fisheries_trap 0.2032 0.3795 0.5354 0.5924
fisheries_trawl -0.06257 0.3559 -0.1758 0.8604
fisheries_net -774.2 85927 -0.00901 0.9928
fisheries_dredge 0.06996 0.7317 0.09561 0.9238 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.2 29.6 7.65 8.81 2.77 14.6 3.86 13.2 19.3 26.5 13 1.13 1.38 1 1.68

Monoculopsis longicornis

## SDM for: monoculopsis_longicornis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.63 
## Tjur's pseudo-R2 is: 0.48 
## Pearson's pseudo-R2 is: 0.43
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -86.66 38773 -0.002235 0.9982
om -1.559 3.285 -0.4747 0.635
gravel -28.24 26931 -0.001049 0.9992
silt 0.01079 2.344 0.004602 0.9963
clay -387.6 208414 -0.00186 0.9985
arsenic 1.75 2.683 0.6522 0.5143
cadmium -3.345 3.536 -0.9461 0.3441
copper 4.331 3.089 1.402 0.1609
iron -3.728 5.918 -0.63 0.5287
manganese -3.273 11.72 -0.2792 0.7801
mercury -1.005 3.995 -0.2515 0.8014 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.57 1 2.08 1 1.78 2.02 2.21 3.62 3.96 2.49

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -172.4 967725 -0.0001782 0.9999
aquaculture -390 12814618 -3.043e-05 1
city 74.86 6308431 1.187e-05 1
dredging_collect 552.1 3103581 0.0001779 0.9999
dredging_dump 659 6308807 0.0001045 0.9999
industry 128.5 4011008 3.203e-05 1
shipping_mooring -117.2 6354516 -1.844e-05 1
shipping_traffic 45.96 6798871 6.759e-06 1
sewers_rain 499.8 7902243 6.325e-05 0.9999
sewers_waste -623.7 15087169 -4.134e-05 1
wharves_city -162 6443652 -2.514e-05 1
wharves_industry -1230 11358651 -0.0001083 0.9999
fisheries_trap -2.09 435485 -4.799e-06 1
fisheries_trawl 29.63 8018744 3.695e-06 1
fisheries_net 7.453 401172 1.858e-05 1
fisheries_dredge -23.63 1800714 -1.312e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 120 64.4 33.7 67.1 38.1 72.6 79.9 101 180 68.3 124 2.19 66.5 1.6 37.9

Muculus musculus discors

## SDM for: muculus_musculus_discors

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -182.6 473133 -0.0003859 0.9997
om -134.2 358347 -0.0003744 0.9997
gravel -4.482 286106 -1.566e-05 1
silt 64.12 285563 0.0002245 0.9998
clay -105.7 1398946 -7.555e-05 0.9999
arsenic 5.142 431968 1.19e-05 1
cadmium -40.83 219759 -0.0001858 0.9999
copper 62.63 258197 0.0002426 0.9998
iron 9.686 117058 8.274e-05 0.9999
manganese -19.25 328394 -5.861e-05 1
mercury -69.44 301137 -0.0002306 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.88 1.28 4.22 1.51 3.79 3.13 4.32 1.92 4.01 3.3

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -35.16 72323 -0.0004862 0.9996
aquaculture -24 1358385 -1.767e-05 1
city -12.59 1297078 -9.708e-06 1
dredging_collect -14.92 851895 -1.751e-05 1
dredging_dump -6.445 662645 -9.726e-06 1
industry 11.9 525131 2.266e-05 1
shipping_mooring -27.69 1438980 -1.924e-05 1
shipping_traffic -18.11 338086 -5.356e-05 1
sewers_rain -19.75 1022136 -1.932e-05 1
sewers_waste 24.54 1349796 1.818e-05 1
wharves_city 28.32 1807498 1.567e-05 1
wharves_industry 31.49 777801 4.048e-05 1
fisheries_trap -0.6731 83030 -8.106e-06 1
fisheries_trawl 10.88 67868 0.0001604 0.9999
fisheries_net 3.765 70648 5.329e-05 1
fisheries_dredge 0.6407 190947 3.355e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 27.5 18.2 14.2 11 9.45 23.7 5.79 15.1 21.9 25.4 12.8 1.63 4.13 1.25 2.35

Mytilus sp

## SDM for: mytilus_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.2 
## Tjur's pseudo-R2 is: 0.17 
## Pearson's pseudo-R2 is: 0.16
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.88 0.688 -4.186 2.841e-05 * * *
om -0.1948 0.7785 -0.2502 0.8024
gravel 0.1476 0.4662 0.3167 0.7515
silt 0.6068 0.787 0.771 0.4407
clay -0.9339 1.514 -0.6167 0.5374
arsenic -1.527 1.144 -1.335 0.182
cadmium 0.5376 0.5464 0.9839 0.3251
copper 2.405 0.898 2.678 0.007406 * *
iron -2.358 1.374 -1.716 0.08613
manganese 0.2591 1.014 0.2556 0.7983
mercury -2.226 1.131 -1.968 0.0491 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.97 1.21 1.94 1.07 1.75 1.66 2.77 2.95 1.95 1.83

Influence indices

## McFadden's pseudo-R2 is: 0.35 
## Tjur's pseudo-R2 is: 0.3 
## Pearson's pseudo-R2 is: 0.29
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -64.65 7059 -0.009159 0.9927
aquaculture 6.408 6.141 1.044 0.2967
city 1.343 3.707 0.3622 0.7172
dredging_collect -0.17 3.938 -0.04317 0.9656
dredging_dump 3.818 6.346 0.6016 0.5475
industry -3.551 2.632 -1.349 0.1773
shipping_mooring 1.824 4.812 0.379 0.7047
shipping_traffic -2.772 1.842 -1.504 0.1325
sewers_rain 3.33 5.371 0.62 0.5352
sewers_waste -0.7764 8.056 -0.09638 0.9232
wharves_city -2.294 5.588 -0.4105 0.6815
wharves_industry 1.977 5.775 0.3424 0.7321
fisheries_trap -0.4839 0.4738 -1.021 0.3071
fisheries_trawl 0.2315 0.5303 0.4366 0.6624
fisheries_net -631 73197 -0.008621 0.9931
fisheries_dredge 0.5779 1.306 0.4426 0.658 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 15.6 11.3 10.5 17.6 7.11 11.6 5.54 16 22.5 16.2 15.9 1.08 1.33 1 2.22

Nematoda

## SDM for: nematoda

Abiotic parameters

## McFadden's pseudo-R2 is: 0.47 
## Tjur's pseudo-R2 is: 0.54 
## Pearson's pseudo-R2 is: 0.55
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.457 1.067 -2.302 0.02132 *
om -0.8545 0.7232 -1.182 0.2374
gravel -0.2797 0.3777 -0.7405 0.459
silt -0.6092 0.7289 -0.8358 0.4033
clay -9.586 5.489 -1.746 0.08074
arsenic 1.045 0.536 1.95 0.05112
cadmium -1.131 0.5194 -2.177 0.02946 *
copper -1.242 0.8724 -1.424 0.1546
iron -2.346 1.254 -1.871 0.06139
manganese 1.621 0.8759 1.851 0.06417
mercury 0.3263 0.5635 0.5791 0.5625 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.78 1.34 2.09 1.28 1.61 1.44 2.37 2.51 2.55 1.67

Influence indices

## McFadden's pseudo-R2 is: 0.33 
## Tjur's pseudo-R2 is: 0.39 
## Pearson's pseudo-R2 is: 0.38
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2484 0.4321 -0.5749 0.5654
aquaculture -1.278 2.837 -0.4504 0.6525
city -1.194 2.648 -0.4508 0.6522
dredging_collect 1.334 2.115 0.6306 0.5283
dredging_dump -1.361 2.363 -0.5761 0.5646
industry 0.4921 1.263 0.3896 0.6969
shipping_mooring -3.203 2.485 -1.289 0.1974
shipping_traffic 2.436 1.385 1.758 0.07873
sewers_rain -1.229 2.759 -0.4454 0.656
sewers_waste 2.189 3.69 0.5934 0.5529
wharves_city 1.855 3.385 0.548 0.5837
wharves_industry -3.37 3.321 -1.015 0.3102
fisheries_trap -0.112 0.3071 -0.3647 0.7153
fisheries_trawl -0.6734 0.4 -1.684 0.09226
fisheries_net 0.5368 2.741 0.1958 0.8448
fisheries_dredge 0.1026 1.081 0.09483 0.9244 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.7 9 6.77 7.41 4.82 8.07 5.09 10 13.4 10.6 10.9 1.09 1.41 1 2.02

Nemertea

## SDM for: nemertea

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -92.72 150483 -0.0006162 0.9995
om -51.01 168846 -0.0003021 0.9998
gravel 26.9 70521 0.0003815 0.9997
silt 22.7 175221 0.0001296 0.9999
clay 2.983 256019 1.165e-05 1
arsenic -38.2 199649 -0.0001913 0.9998
cadmium 29.85 97550 0.000306 0.9998
copper 30.54 185178 0.0001649 0.9999
iron 7.023 449909 1.561e-05 1
manganese -28.08 250267 -0.0001122 0.9999
mercury 17.88 178947 9.994e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.01 3.95 3.8 1.1 2.74 2.8 4.52 5.78 4.55 3.87

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -67.54 140643 -0.0004802 0.9996
aquaculture 71.34 4842619 1.473e-05 1
city 145.3 10402474 1.397e-05 1
dredging_collect 77.29 774144 9.983e-05 0.9999
dredging_dump -69.13 2155231 -3.207e-05 1
industry 46.71 2178614 2.144e-05 1
shipping_mooring 71.66 2724611 2.63e-05 1
shipping_traffic 44.96 2905192 1.548e-05 1
sewers_rain -7.165 4554958 -1.573e-06 1
sewers_waste 13.34 2622642 5.087e-06 1
wharves_city -175.1 10700829 -1.637e-05 1
wharves_industry -93.09 1857286 -5.012e-05 1
fisheries_trap 6.767 578628 1.17e-05 1
fisheries_trawl -4.491 150280 -2.989e-05 1
fisheries_net 2.735 404568 6.76e-06 1
fisheries_dredge -8.065 912207 -8.842e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 78.2 177 16.8 40.9 43.9 52.4 45.4 67.8 41.7 170 36.4 8.18 4.68 4.35 5.38

Neoleanira tetragona

## SDM for: neoleanira_tetragona

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -114.9 238426 -0.0004818 0.9996
om 4.49 777544 5.774e-06 1
gravel -11.67 341497 -3.418e-05 1
silt -23.42 276639 -8.466e-05 0.9999
clay 24.81 448080 5.537e-05 1
arsenic 41.32 263325 0.0001569 0.9999
cadmium 32.88 309826 0.0001061 0.9999
copper 4.492 353370 1.271e-05 1
iron -31.99 566876 -5.643e-05 1
manganese -90 805879 -0.0001117 0.9999
mercury -24.23 533750 -4.539e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.29 1.3 3.19 1.12 1.95 3.04 3.38 3.58 5.21 2.24

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -112.7 232083 -0.0004858 0.9996
aquaculture 166.6 1386722 0.0001201 0.9999
city -26.46 3025269 -8.746e-06 1
dredging_collect 270.6 1573361 0.000172 0.9999
dredging_dump 374.1 1185175 0.0003157 0.9997
industry -22.15 1650474 -1.342e-05 1
shipping_mooring 253.7 943754 0.0002688 0.9998
shipping_traffic -53.53 1143947 -4.68e-05 1
sewers_rain 247.1 1710636 0.0001445 0.9999
sewers_waste -220.4 2920215 -7.549e-05 0.9999
wharves_city -140.7 2837107 -4.96e-05 1
wharves_industry -654.1 2237862 -0.0002923 0.9998
fisheries_trap -27.78 264305 -0.0001051 0.9999
fisheries_trawl 5.439 410747 1.324e-05 1
fisheries_net 0.5491 174955 3.139e-06 1
fisheries_dredge -10.36 321942 -3.218e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 14.8 39.7 22.8 17.1 30.9 9.77 19.6 19.1 28.9 32.9 33.8 1.26 3.14 1.14 2.07

Nephtyidae spp

## SDM for: nephtyidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.33 
## Tjur's pseudo-R2 is: 0.24 
## Pearson's pseudo-R2 is: 0.24
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -129.7 22067 -0.005877 0.9953
om -1.639 1.008 -1.627 0.1038
gravel -32.36 16686 -0.001939 0.9985
silt 0.8808 0.8661 1.017 0.3092
clay -638.5 117715 -0.005424 0.9957
arsenic 1.07 0.7221 1.482 0.1383
cadmium -0.1174 0.6415 -0.183 0.8548
copper -0.1745 1.097 -0.1591 0.8736
iron -3.867 2.732 -1.415 0.1569
manganese 2.61 1.752 1.49 0.1362
mercury -0.207 0.913 -0.2268 0.8206 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.72 1 1.7 1 2.08 1.45 2.5 4.84 4.22 2.02

Influence indices

## McFadden's pseudo-R2 is: -8.94 
## Tjur's pseudo-R2 is: 0.15 
## Pearson's pseudo-R2 is: 0.07
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.859e+15 7496963 -2.48e+08 0 * * *
aquaculture -1.65e+15 66771996 -24705435 0 * * *
city 1.632e+15 60515998 26960600 0 * * *
dredging_collect 2.193e+14 47886179 4580309 0 * * *
dredging_dump -1.273e+14 55589796 -2289661 0 * * *
industry 2.026e+15 30193191 67105166 0 * * *
shipping_mooring -1.194e+15 51224704 -23309623 0 * * *
shipping_traffic -9.411e+14 22815885 -41246884 0 * * *
sewers_rain 1.998e+15 67164046 29742764 0 * * *
sewers_waste -1.87e+15 90359677 -20691261 0 * * *
wharves_city -2.219e+15 72465526 -30623705 0 * * *
wharves_industry -5.91e+14 79067645 -7474226 0 * * *
fisheries_trap -2.403e+14 7277539 -33026188 0 * * *
fisheries_trawl 3.983e+14 8821984 45152815 0 * * *
fisheries_net -2.655e+14 7219163 -36774956 0 * * *
fisheries_dredge -4.199e+14 19454028 -21586018 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Nephtys caeca

## SDM for: nephtys_caeca

Abiotic parameters

## McFadden's pseudo-R2 is: 0.35 
## Tjur's pseudo-R2 is: 0.37 
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -373.6 58826 -0.006351 0.9949
om -0.9548 1.092 -0.8746 0.3818
gravel 0.485 0.414 1.172 0.2413
silt -0.08846 0.9323 -0.09489 0.9244
clay -2021 321005 -0.006295 0.995
arsenic -0.9707 1.413 -0.6872 0.492
cadmium 1.709 0.7253 2.357 0.01842 *
copper -0.09584 0.8005 -0.1197 0.9047
iron 1.088 0.5293 2.056 0.03978 *
manganese -0.5698 1.018 -0.5598 0.5756
mercury -0.7142 1.121 -0.6369 0.5242 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.99 1.34 1.88 1 2.09 1.99 1.99 1.63 1.54 1.6

Influence indices

## McFadden's pseudo-R2 is: 0.54 
## Tjur's pseudo-R2 is: 0.45 
## Pearson's pseudo-R2 is: 0.43
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.18 22.7 -0.4485 0.6538
aquaculture 21.39 11.2 1.91 0.05608
city 6.18 5.218 1.184 0.2363
dredging_collect -9.698 8.327 -1.165 0.2442
dredging_dump 3.272 6.1 0.5363 0.5918
industry -14.47 6.897 -2.098 0.03589 *
shipping_mooring 9.344 5.286 1.768 0.07712
shipping_traffic -0.8441 2.157 -0.3914 0.6955
sewers_rain -13.46 9.76 -1.379 0.1678
sewers_waste 11.36 13.59 0.8364 0.4029
wharves_city -12.11 7.535 -1.607 0.1081
wharves_industry 19.01 11.79 1.612 0.1069
fisheries_trap 1.024 0.4634 2.21 0.02712 *
fisheries_trawl -0.7393 1.216 -0.6081 0.5431
fisheries_net -0.6575 230.6 -0.002851 0.9977
fisheries_dredge -20.53 11.75 -1.747 0.08062 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 21.1 13.2 19.7 14.6 13 9.93 4.32 14.6 21.7 20.4 26.7 1.67 2.75 1 3.41

Nephtys incisa

## SDM for: nephtys_incisa

Abiotic parameters

## McFadden's pseudo-R2 is: 0.26 
## Tjur's pseudo-R2 is: 0.29 
## Pearson's pseudo-R2 is: 0.29
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.318 0.4156 -3.17 0.001524 * *
om 1.799 0.6481 2.776 0.005495 * *
gravel 0.1471 0.3153 0.4664 0.6409
silt -0.04529 0.601 -0.07535 0.9399
clay -0.0824 1.533 -0.05375 0.9571
arsenic 0.75 0.4802 1.562 0.1183
cadmium -1.761 0.7329 -2.403 0.01624 *
copper 0.2368 0.7444 0.3181 0.7504
iron -0.9342 0.7163 -1.304 0.1921
manganese -0.8585 0.7242 -1.186 0.2358
mercury 0.3689 0.5156 0.7155 0.4743 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.43 1.44 2.24 1.05 1.68 2.32 2.62 2.06 2.45 1.82

Influence indices

## McFadden's pseudo-R2 is: 0.32 
## Tjur's pseudo-R2 is: 0.32 
## Pearson's pseudo-R2 is: 0.32
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -73.61 6436 -0.01144 0.9909
aquaculture -1.622 2.674 -0.6065 0.5442
city -26.86 14.13 -1.902 0.05722
dredging_collect 6.129 2.863 2.141 0.03228 *
dredging_dump 3.482 3.398 1.025 0.3055
industry 1.757 1.268 1.385 0.1661
shipping_mooring 9.422 5.379 1.752 0.07981
shipping_traffic -0.7808 1.453 -0.5376 0.5909
sewers_rain 9.013 4.595 1.962 0.04981 *
sewers_waste -16.2 6.962 -2.327 0.01999 *
wharves_city 19.94 11.41 1.747 0.0807
wharves_industry -9.671 5.031 -1.922 0.05457
fisheries_trap 0.1523 0.494 0.3083 0.7579
fisheries_trawl 0.01495 0.3058 0.04889 0.961
fisheries_net -724.7 66734 -0.01086 0.9913
fisheries_dredge -0.6645 0.6645 -1 0.3173 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.2 39.5 8.86 10.7 2.73 20.3 4.68 16.6 26.8 34.2 15.6 1.15 1.4 1 1.72

Nephtys sp

## SDM for: nephtys_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.09 
## Tjur's pseudo-R2 is: 0.01 
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -124.5 25087 -0.004963 0.996
om -0.3531 1.404 -0.2514 0.8015
gravel -31.13 19188 -0.001622 0.9987
silt 0.2853 1.383 0.2064 0.8365
clay -611.9 133753 -0.004575 0.9963
arsenic 0.2589 0.9203 0.2813 0.7785
cadmium -0.1391 1.244 -0.1118 0.911
copper 0.6144 1.615 0.3804 0.7037
iron -0.8727 2.341 -0.3728 0.7093
manganese -0.2432 1.998 -0.1217 0.9031
mercury -0.2722 1.452 -0.1875 0.8512 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.61 1 1.63 1 1.46 1.6 2.26 2.54 2.1 1.54

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -185 498200 -0.0003713 0.9997
aquaculture 409.2 20784553 1.969e-05 1
city -253.2 26358717 -9.605e-06 1
dredging_collect -3.953 7931459 -4.983e-07 1
dredging_dump -60.55 5450091 -1.111e-05 1
industry 67.95 4340815 1.565e-05 1
shipping_mooring 196.7 13179186 1.493e-05 1
shipping_traffic -37.57 2250903 -1.669e-05 1
sewers_rain -86.76 3594033 -2.414e-05 1
sewers_waste 353.4 10172112 3.474e-05 1
wharves_city 283.6 26524927 1.069e-05 1
wharves_industry -159.2 4820664 -3.303e-05 1
fisheries_trap -6.987 2822742 -2.475e-06 1
fisheries_trawl -10.63 494830 -2.148e-05 1
fisheries_net 1.548 266450 5.808e-06 1
fisheries_dredge -133.7 2704897 -4.943e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 358 187 56.8 36 44.1 243 23.5 49.1 154 184 32.6 9.03 3.18 1.06 7.81

Nuculana minuta

## SDM for: nuculana_minuta

Abiotic parameters

## McFadden's pseudo-R2 is: 0.16 
## Tjur's pseudo-R2 is: 0.07 
## Pearson's pseudo-R2 is: 0.05
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -324.9 50727 -0.006405 0.9949
om 0.4017 0.8142 0.4933 0.6218
gravel 0.2555 0.388 0.6586 0.5102
silt 0.4505 0.8164 0.5518 0.5811
clay -1758 276808 -0.00635 0.9949
arsenic -0.7582 1.962 -0.3864 0.6992
cadmium -0.518 0.7624 -0.6794 0.4969
copper -0.1736 1.005 -0.1727 0.8629
iron -0.6751 1.007 -0.6703 0.5027
manganese 0.3039 1.439 0.2113 0.8327
mercury -0.5311 0.9704 -0.5473 0.5842 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.91 1.32 2.08 1 1.92 1.53 1.89 1.85 2.46 1.69

Influence indices

## McFadden's pseudo-R2 is: -4.42 
## Tjur's pseudo-R2 is: 0.61 
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.432e+15 7496963 -191056064 0 * * *
aquaculture 4.56e+15 66771996 68298016 0 * * *
city 2.319e+15 60515998 38328191 0 * * *
dredging_collect 5.398e+15 47886179 112725040 0 * * *
dredging_dump 1.47e+15 55589796 26451871 0 * * *
industry -1.161e+15 30193191 -38447880 0 * * *
shipping_mooring 5.288e+15 51224704 103233248 0 * * *
shipping_traffic 1.002e+15 22815885 43908825 0 * * *
sewers_rain 2.122e+15 67164046 31596831 0 * * *
sewers_waste -2.1e+15 90359677 -23235378 0 * * *
wharves_city -5.124e+15 72465526 -70711503 0 * * *
wharves_industry -7.384e+15 79067645 -93385449 0 * * *
fisheries_trap -4.244e+14 7277539 -58316903 0 * * *
fisheries_trawl -1.541e+14 8821984 -17470949 0 * * *
fisheries_net 3.806e+14 7219163 52727565 0 * * *
fisheries_dredge 7.068e+14 19454028 36330369 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Nymphonidae spp

## SDM for: nymphonidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -95.8 274909 -0.0003485 0.9997
om -10.42 524836 -1.985e-05 1
gravel 6.602 77488 8.52e-05 0.9999
silt -1.753 229514 -7.637e-06 1
clay 23.84 1153878 2.066e-05 1
arsenic -107.1 345443 -0.0003101 0.9998
cadmium -11.83 187356 -6.316e-05 0.9999
copper 7.065 286431 2.467e-05 1
iron -26.82 273093 -9.822e-05 0.9999
manganese 42.82 545233 7.854e-05 0.9999
mercury 10.22 290476 3.52e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.62 2.62 3.86 2 1.76 5.63 3.51 3.92 8.2 3.55

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -116.6 240165 -0.0004857 0.9996
aquaculture -168.3 1102516 -0.0001526 0.9999
city 251.4 1289120 0.000195 0.9998
dredging_collect 9.64 1661898 5.801e-06 1
dredging_dump 151.3 1022875 0.000148 0.9999
industry -34.59 566055 -6.111e-05 1
shipping_mooring 2.434 869643 2.799e-06 1
shipping_traffic -48.51 852461 -5.69e-05 1
sewers_rain 332.8 1136950 0.0002927 0.9998
sewers_waste -562 1673553 -0.0003358 0.9997
wharves_city -312.7 1361612 -0.0002297 0.9998
wharves_industry 27.32 2040076 1.339e-05 1
fisheries_trap 18.7 82254 0.0002273 0.9998
fisheries_trawl 16.11 167002 9.649e-05 0.9999
fisheries_net 3.69 162216 2.275e-05 1
fisheries_dredge 30.81 692729 4.448e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 12.6 20.1 19 12.9 6.88 7.74 11 11.3 15.7 23.9 24.1 2.03 2 1.04 6.34

Oenopota sp

## SDM for: oenopota_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.32 
## Tjur's pseudo-R2 is: 0.27 
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.727 1.254 -3.769 0.0001638 * * *
om -1.065 0.9046 -1.178 0.2389
gravel 0.8247 0.5969 1.382 0.1671
silt 1.73 1.044 1.657 0.09746
clay -0.9584 2.099 -0.4565 0.648
arsenic 0.2882 1.065 0.2706 0.7867
cadmium -0.2272 0.9921 -0.229 0.8188
copper 1.853 1.417 1.308 0.1909
iron -5.839 2.913 -2.005 0.04502 *
manganese 3.167 1.721 1.84 0.06581
mercury -1.661 1.068 -1.555 0.1199 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.76 1.58 2.29 1.06 2.43 1.92 3.37 5.23 4.36 1.75

Influence indices

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.16 
## Pearson's pseudo-R2 is: 0.14
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -556.1 118321 -0.0047 0.9963
aquaculture 6.541 8.578 0.7625 0.4458
city 10.11 7.582 1.333 0.1826
dredging_collect -5.998 6.758 -0.8876 0.3747
dredging_dump -0.8892 5.019 -0.1772 0.8594
industry 2.124 2.58 0.8231 0.4104
shipping_mooring 2.109 6.197 0.3404 0.7336
shipping_traffic -1.708 4.294 -0.3978 0.6908
sewers_rain -1.5 5.9 -0.2543 0.7993
sewers_waste 4.891 8.401 0.5822 0.5604
wharves_city -9.068 8.347 -1.086 0.2773
wharves_industry 7.18 9.081 0.7907 0.4291
fisheries_trap 0.5012 0.6399 0.7832 0.4335
fisheries_trawl -1845 320648 -0.005754 0.9954
fisheries_net -651.4 852238 -0.0007643 0.9994
fisheries_dredge 2.187 2.412 0.9069 0.3645 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 16.6 15.7 14.8 12.2 7.99 11.1 12.4 12 18.1 15.7 22.6 1.59 1 1 2.32

Oligochaeta

## SDM for: oligochaeta

Abiotic parameters

## McFadden's pseudo-R2 is: 0.36 
## Tjur's pseudo-R2 is: 0.32 
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -109.9 15471 -0.007102 0.9943
om 0.8845 0.7413 1.193 0.2328
gravel -28.92 9547 -0.003029 0.9976
silt 0.1045 1.069 0.09777 0.9221
clay -541.4 83177 -0.006509 0.9948
arsenic -2.147 1.357 -1.583 0.1135
cadmium 0.5475 0.6447 0.8493 0.3957
copper 0.6134 0.7771 0.7894 0.4299
iron 0.03174 1.053 0.03013 0.976
manganese -0.2085 0.8471 -0.2462 0.8055
mercury 1.166 0.694 1.681 0.09286 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.88 1 2.12 1 2 1.37 1.7 1.89 1.84 1.59

Influence indices

## McFadden's pseudo-R2 is: -10.41 
## Tjur's pseudo-R2 is: 0.43 
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.458e+15 7496963 -327858358 0 * * *
aquaculture -3.795e+15 66771996 -56833032 0 * * *
city -1.047e+15 60515998 -17296953 0 * * *
dredging_collect -3.77e+14 47886179 -7873313 0 * * *
dredging_dump 1.842e+15 55589796 33132528 0 * * *
industry 5.258e+14 30193191 17414925 0 * * *
shipping_mooring -1.942e+15 51224704 -37903638 0 * * *
shipping_traffic 8.97e+14 22815885 39314681 0 * * *
sewers_rain -3.478e+15 67164046 -51785502 0 * * *
sewers_waste 2.334e+15 90359677 25825931 0 * * *
wharves_city 2.102e+15 72465526 29005272 0 * * *
wharves_industry -1.521e+15 79067645 -19237755 0 * * *
fisheries_trap -6.339e+13 7277539 -8710477 0 * * *
fisheries_trawl 2.722e+14 8821984 30855581 0 * * *
fisheries_net 2.094e+14 7219163 29005761 0 * * *
fisheries_dredge 1.608e+14 19454028 8265185 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Ophelia limacina

## SDM for: ophelia_limacina

Abiotic parameters

## McFadden's pseudo-R2 is: 0.17 
## Tjur's pseudo-R2 is: 0.11 
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -363.9 59835 -0.006082 0.9951
om -1.227 1.128 -1.088 0.2766
gravel 0.6418 0.4589 1.399 0.162
silt 0.42 0.9173 0.4578 0.6471
clay -1966 326512 -0.006023 0.9952
arsenic 0.302 0.8228 0.367 0.7136
cadmium 0.03719 1.043 0.03566 0.9716
copper 0.7528 1.378 0.5462 0.5849
iron 0.01492 1.618 0.009225 0.9926
manganese -1.309 1.266 -1.034 0.3013
mercury 0.7863 0.7895 0.996 0.3192 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.86 1.58 1.88 1 1.57 1.98 2.81 2.38 1.93 1.63

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -158.9 249610 -0.0006365 0.9995
aquaculture 405.9 1950174 0.0002081 0.9998
city 301.4 2840020 0.0001061 0.9999
dredging_collect 135.7 959463 0.0001414 0.9999
dredging_dump 5.799 575818 1.007e-05 1
industry -74.11 421515 -0.0001758 0.9999
shipping_mooring 274.5 1052677 0.0002608 0.9998
shipping_traffic 111.3 519451 0.0002143 0.9998
sewers_rain -117.6 2010549 -5.851e-05 1
sewers_waste 150.3 2163606 6.946e-05 0.9999
wharves_city -390.6 2725877 -0.0001433 0.9999
wharves_industry -201 1326411 -0.0001515 0.9999
fisheries_trap -119.9 978061 -0.0001226 0.9999
fisheries_trawl -35.69 137617 -0.0002594 0.9998
fisheries_net 4.749 225552 2.106e-05 1
fisheries_dredge -224.5 714188 -0.0003143 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 48.1 50.9 20 10.7 8.01 29.2 9.84 40 46.4 48.4 25.6 5.92 3.98 1.47 2.7

Opheliidae spp

## SDM for: opheliidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.6 
## Tjur's pseudo-R2 is: 0.41 
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -135.2 121671 -0.001111 0.9991
om -1.282 4.632 -0.2768 0.782
gravel 2.394 3.544 0.6755 0.4993
silt 4.39 9.869 0.4448 0.6564
clay -641.9 663941 -0.0009669 0.9992
arsenic 4.284 6.077 0.7051 0.4808
cadmium 3.629 6.339 0.5726 0.5669
copper -2.033 8.999 -0.2259 0.8213
iron 1.842 3.289 0.5599 0.5755
manganese -11.17 26.08 -0.4282 0.6685
mercury -10.87 9.945 -1.093 0.2745 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.01 7.87 10.5 1 3.57 5 5.13 3.42 6.3 3.64

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -452.8 1024570 -0.0004419 0.9996
aquaculture -224.8 2536378 -8.862e-05 0.9999
city -997.3 4150230 -0.0002403 0.9998
dredging_collect -1036 4226919 -0.000245 0.9998
dredging_dump -439.8 4577201 -9.609e-05 0.9999
industry 298.1 1843809 0.0001617 0.9999
shipping_mooring -1147 4142139 -0.0002769 0.9998
shipping_traffic -169.8 500126 -0.0003395 0.9997
sewers_rain -1299 5298398 -0.0002451 0.9998
sewers_waste 2027 8653272 0.0002343 0.9998
wharves_city 1665 7533631 0.000221 0.9998
wharves_industry 1103 5599664 0.000197 0.9998
fisheries_trap -373.1 1986454 -0.0001878 0.9999
fisheries_trawl 36.4 143366 0.0002539 0.9998
fisheries_net 100.6 6462729 1.557e-05 1
fisheries_dredge -156.8 433587 -0.0003617 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 44.9 58.9 82 83.8 32 67.9 10.3 102 172 109 106 3.36 4.8 1 5.91

Ophiopholis aculeata

## SDM for: ophiopholis_aculeata

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -381.8 929534 -0.0004108 0.9997
om -166.7 428880 -0.0003886 0.9997
gravel 19.89 290335 6.851e-05 0.9999
silt 171.2 433268 0.0003951 0.9997
clay -728.3 2170437 -0.0003355 0.9997
arsenic 38.32 138267 0.0002771 0.9998
cadmium -91.9 301965 -0.0003043 0.9998
copper 38.28 179382 0.0002134 0.9998
iron 1.822 89274 2.041e-05 1
manganese 66.61 351870 0.0001893 0.9998
mercury -206.6 565129 -0.0003656 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.54 1.07 6.94 1.69 1.86 3.36 1.95 1.61 2.4 5.19

Influence indices

## McFadden's pseudo-R2 is: -2.79 
## Tjur's pseudo-R2 is: 0.5 
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.799e+15 7496963 -373354674 0 * * *
aquaculture 2.674e+15 66771996 40044326 0 * * *
city -4.437e+14 60515998 -7332363 0 * * *
dredging_collect 2.276e+15 47886179 47539070 0 * * *
dredging_dump 3.273e+15 55589796 58870875 0 * * *
industry 1.1e+15 30193191 36425419 0 * * *
shipping_mooring 2.533e+15 51224704 49448042 0 * * *
shipping_traffic -2.486e+15 22815885 -108944284 0 * * *
sewers_rain 3.584e+15 67164046 53358494 0 * * *
sewers_waste -1.608e+15 90359677 -17797607 0 * * *
wharves_city -1.468e+15 72465526 -20259531 0 * * *
wharves_industry -5.806e+15 79067645 -73434045 0 * * *
fisheries_trap 1.199e+14 7277539 16473947 0 * * *
fisheries_trawl 4.707e+14 8821984 53355115 0 * * *
fisheries_net 1.527e+14 7219163 21145155 0 * * *
fisheries_dredge -8.88e+14 19454028 -45646005 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Ophiura robusta

## SDM for: ophiura_robusta

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1451 2311942 -0.0006275 0.9995
om -896 1448991 -0.0006183 0.9995
gravel -28.47 108848 -0.0002616 0.9998
silt -3.358 225083 -1.492e-05 1
clay 310.9 618796 0.0005024 0.9996
arsenic -324.2 1219691 -0.0002658 0.9998
cadmium -168 373256 -0.00045 0.9996
copper 243.7 426199 0.0005717 0.9995
iron 154.6 286481 0.0005396 0.9996
manganese -671 1230015 -0.0005456 0.9996
mercury -12.74 372098 -3.425e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 5.32 3.22 3.41 1.89 2.99 4.47 6.59 4.74 7.2 3.77

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -377.9 1006500 -0.0003755 0.9997
aquaculture -368.3 1523576 -0.0002417 0.9998
city 791.8 2586677 0.0003061 0.9998
dredging_collect 267.7 1213713 0.0002206 0.9998
dredging_dump 646 2262021 0.0002856 0.9998
industry -233.5 787343 -0.0002965 0.9998
shipping_mooring 65.96 1124570 5.865e-05 1
shipping_traffic -130.6 531926 -0.0002455 0.9998
sewers_rain 1156 3052363 0.0003788 0.9997
sewers_waste -2038 5104173 -0.0003993 0.9997
wharves_city -1135 3672102 -0.000309 0.9998
wharves_industry -252.1 1745371 -0.0001444 0.9999
fisheries_trap 24.99 309605 8.071e-05 0.9999
fisheries_trawl 7.673 63749 0.0001204 0.9999
fisheries_net -1.589 6466823 -2.457e-07 1
fisheries_dredge -333.7 1013326 -0.0003293 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 18.3 38.3 17.7 34.2 8.88 15.2 9.88 52.2 67 56.5 26.3 1.87 2.87 1.12 5.5

Orchomenella minuta

## SDM for: orchomenella_minuta

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -96.53 269243 -0.0003585 0.9997
om 41.74 341110 0.0001224 0.9999
gravel 5.826 210168 2.772e-05 1
silt 24.89 423176 5.881e-05 1
clay -20.02 1126809 -1.777e-05 1
arsenic -16.27 660117 -2.465e-05 1
cadmium -61.42 304738 -0.0002015 0.9998
copper 34.47 333143 0.0001035 0.9999
iron 0.4691 412637 1.137e-06 1
manganese -29.78 289510 -0.0001029 0.9999
mercury 17.92 320937 5.585e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 5.81 2.22 4.48 1.89 6.37 6.94 8.24 6.33 4.26 5.52

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -47.74 121771 -0.000392 0.9997
aquaculture -66.67 2475590 -2.693e-05 1
city -35.92 960406 -3.741e-05 1
dredging_collect 71.99 1559178 4.617e-05 1
dredging_dump 139.4 1455965 9.576e-05 0.9999
industry 37.12 953112 3.895e-05 1
shipping_mooring 20.65 2689759 7.679e-06 1
shipping_traffic -33.48 725208 -4.617e-05 1
sewers_rain 177.6 1377968 0.0001289 0.9999
sewers_waste -208.7 1842401 -0.0001133 0.9999
wharves_city -5.049 1100192 -4.589e-06 1
wharves_industry -216.8 1689674 -0.0001283 0.9999
fisheries_trap 1.871 392615 4.765e-06 1
fisheries_trawl -5.469 493752 -1.108e-05 1
fisheries_net -3.411 106286 -3.209e-05 1
fisheries_dredge -3.425 458731 -7.466e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 26.1 11.4 17.1 16.6 9.62 30.8 9 21.5 21 13.2 19.2 1.89 4.57 1.08 4.8

Ostracoda

## SDM for: ostracoda

Abiotic parameters

## McFadden's pseudo-R2 is: 0.2 
## Tjur's pseudo-R2 is: 0.21 
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.77 0.5656 -3.13 0.001748 * *
om 0.2179 0.6015 0.3623 0.7171
gravel -0.7979 0.7211 -1.107 0.2685
silt 0.196 0.5838 0.3357 0.7371
clay -1.496 2.496 -0.5992 0.5491
arsenic 0.7567 0.5011 1.51 0.131
cadmium -0.9634 0.6149 -1.567 0.1171
copper 0.9222 0.7102 1.299 0.1941
iron -0.9031 0.8186 -1.103 0.2699
manganese -1.228 0.8245 -1.49 0.1363
mercury -0.2323 0.5896 -0.394 0.6936 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.94 1.11 1.89 1.05 1.66 1.95 2.54 2.2 2.05 1.64

Influence indices

## McFadden's pseudo-R2 is: 0.26 
## Tjur's pseudo-R2 is: 0.27 
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -48.67 5821 -0.008361 0.9933
aquaculture 3.728 3.769 0.989 0.3226
city 3.856 3.893 0.9904 0.322
dredging_collect 5.293 2.39 2.215 0.02678 *
dredging_dump 1.734 2.836 0.6115 0.5408
industry 0.06059 1.338 0.04527 0.9639
shipping_mooring 3.343 2.69 1.243 0.214
shipping_traffic 2.07 1.197 1.729 0.08389
sewers_rain -0.7488 3.063 -0.2445 0.8069
sewers_waste 0.6046 4.474 0.1351 0.8925
wharves_city -4.582 4.529 -1.012 0.3116
wharves_industry -8.502 4.386 -1.938 0.05259
fisheries_trap -1.141 1.197 -0.9534 0.3404
fisheries_trawl -0.1146 0.3502 -0.3272 0.7436
fisheries_net -487.8 60354 -0.008083 0.9936
fisheries_dredge 1.039 1.081 0.961 0.3365 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 12.8 13.3 8.17 9.81 4.13 9.62 4.16 10.3 15.7 15.2 14.8 1.08 1.47 1 2.01

Pagurus sp

## SDM for: pagurus_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -157.4 448856 -0.0003506 0.9997
om -9.446 244648 -3.861e-05 1
gravel 1.884 549794 3.427e-06 1
silt 25.73 120585 0.0002134 0.9998
clay 3.612 2041819 1.769e-06 1
arsenic -99.38 527677 -0.0001883 0.9998
cadmium 46.64 389449 0.0001198 0.9999
copper 7.77 320737 2.423e-05 1
iron 5.156 458233 1.125e-05 1
manganese -96.32 760707 -0.0001266 0.9999
mercury -33.49 301617 -0.000111 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.49 2.61 2.07 1.42 1.71 5.8 3.83 4.91 6.15 2.97

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -132 261580 -0.0005048 0.9996
aquaculture 29.9 2012429 1.486e-05 1
city -44.81 2524621 -1.775e-05 1
dredging_collect -159.7 1088442 -0.0001467 0.9999
dredging_dump -24.51 2547376 -9.621e-06 1
industry -32.52 1014058 -3.207e-05 1
shipping_mooring -1.868 1479903 -1.262e-06 1
shipping_traffic -16.82 413635 -4.066e-05 1
sewers_rain -9.975 1818813 -5.484e-06 1
sewers_waste 47.74 3083842 1.548e-05 1
wharves_city 64.8 3296940 1.965e-05 1
wharves_industry 194.3 1348004 0.0001441 0.9999
fisheries_trap -166.4 673787 -0.0002469 0.9998
fisheries_trawl -108.3 748721 -0.0001446 0.9999
fisheries_net 5.202 183416 2.836e-05 1
fisheries_dredge 51.23 177084 0.0002893 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 30.6 29.4 14.3 34.2 9.02 22.4 6.04 31.7 53.2 36.3 18.3 2.25 2.44 1.2 2.14

Pagurus pubescens

## SDM for: pagurus_pubescens

Abiotic parameters

## McFadden's pseudo-R2 is: 0.7 
## Tjur's pseudo-R2 is: 0.6 
## Pearson's pseudo-R2 is: 0.62
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -49.6 3350 -0.01481 0.9882
om -8.766 17.51 -0.5005 0.6167
gravel 2.332 4.374 0.5332 0.5939
silt -3.38 11.27 -0.2998 0.7643
clay -9.061 18278 -0.0004957 0.9996
arsenic -47.11 62.46 -0.7542 0.4507
cadmium -1.851 3.984 -0.4647 0.6422
copper 4.584 4.348 1.054 0.2917
iron -5.284 18.53 -0.2851 0.7756
manganese -1.207 24.87 -0.04854 0.9613
mercury 2.492 8.335 0.299 0.7649 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 5.18 8.66 8.29 1 9.14 2.6 2.2 10.1 6.25 3.48

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -231.2 436649 -0.0005294 0.9996
aquaculture -318.9 9243941 -3.449e-05 1
city 471.4 11980087 3.935e-05 1
dredging_collect -1.137 5256869 -2.163e-07 1
dredging_dump 178.3 5753943 3.098e-05 1
industry 16.54 664212 2.49e-05 1
shipping_mooring 43.44 13366899 3.25e-06 1
shipping_traffic 60.44 2598359 2.326e-05 1
sewers_rain 607.8 6199623 9.803e-05 0.9999
sewers_waste -1147 5736706 -0.0001999 0.9998
wharves_city -637.9 8470576 -7.531e-05 0.9999
wharves_industry -17.34 3063894 -5.658e-06 1
fisheries_trap -23.95 274194 -8.736e-05 0.9999
fisheries_trawl -69.05 1028122 -6.716e-05 0.9999
fisheries_net 3.257 262419 1.241e-05 1
fisheries_dredge 29.58 336525 8.79e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 163 255 116 122 9.54 241 63.8 132 95.9 192 67.1 2.62 2.5 1.03 6.26

Pandalus montagui

## SDM for: pandalus_montagui

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -35.81 73104 -0.0004899 0.9996
om -0.1494 115788 -1.29e-06 1
gravel -5.465 78899 -6.926e-05 0.9999
silt -0.4603 146274 -3.147e-06 1
clay 2.35 175015 1.343e-05 1
arsenic -8.518 101783 -8.369e-05 0.9999
cadmium -11.21 67009 -0.0001672 0.9999
copper -0.6614 247307 -2.675e-06 1
iron -2.182 111961 -1.949e-05 1
manganese 15.04 143041 0.0001052 0.9999
mercury 7.348 51678 0.0001422 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.45 1.91 3.8 1.15 1.74 1.49 5.61 2.3 4.63 1.89

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -107.6 278609 -0.000386 0.9997
aquaculture 229.3 6217901 3.687e-05 1
city 106.2 3672577 2.891e-05 1
dredging_collect -182 4433016 -4.106e-05 1
dredging_dump 181.1 4784276 3.786e-05 1
industry -127 1393999 -9.109e-05 0.9999
shipping_mooring 31.66 4425802 7.154e-06 1
shipping_traffic 11.24 2550471 4.407e-06 1
sewers_rain -38.7 4661417 -8.302e-06 1
sewers_waste 135.6 6361117 2.132e-05 1
wharves_city -166.9 3683401 -4.531e-05 1
wharves_industry 122.2 4224699 2.892e-05 1
fisheries_trap 0.8093 208879 3.875e-06 1
fisheries_trawl -17.17 1040430 -1.651e-05 1
fisheries_net 3.291 211067 1.559e-05 1
fisheries_dredge -29.93 2234845 -1.339e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 78.2 47.5 57.7 62.1 15.4 52.6 30 57 90.7 49.4 55.8 1.97 7.26 1.38 13.1

Parathyasira equalis

## SDM for: parathyasira_equalis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.4 
## Tjur's pseudo-R2 is: 0.09 
## Pearson's pseudo-R2 is: 0.06
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -28.33 7334 -0.003863 0.9969
om 2.883 8.032 0.3589 0.7196
gravel -26.04 11468 -0.002271 0.9982
silt -1.792 4.549 -0.3939 0.6936
clay -48.61 35981 -0.001351 0.9989
arsenic -1.186 25.9 -0.04579 0.9635
cadmium 1.978 4.607 0.4293 0.6677
copper -0.6456 6.62 -0.09751 0.9223
iron -2.897 11.07 -0.2618 0.7935
manganese -4.971 18.11 -0.2745 0.7837
mercury -3.972 8.918 -0.4454 0.656 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 6.05 1 4.36 1 4.03 3.02 2.08 2.48 2.39 3.25

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -140.7 328669 -0.0004281 0.9997
aquaculture 229.6 10937180 2.1e-05 1
city 273.9 8728790 3.137e-05 1
dredging_collect -365.4 6986522 -5.23e-05 1
dredging_dump -153.6 3458238 -4.441e-05 1
industry -59.61 4474646 -1.332e-05 1
shipping_mooring 167.9 8742986 1.92e-05 1
shipping_traffic 17.97 1883919 9.537e-06 1
sewers_rain -66.53 2693857 -2.47e-05 1
sewers_waste 177 3450483 5.128e-05 1
wharves_city -232.7 12961639 -1.795e-05 1
wharves_industry 536.6 5483854 9.786e-05 0.9999
fisheries_trap -17.42 157690 -0.0001105 0.9999
fisheries_trawl -21.83 446444 -4.889e-05 1
fisheries_net 11.02 206739 5.329e-05 1
fisheries_dredge 204.7 653920 0.0003131 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 197 115 110 48.9 47 149 30.4 48.4 60.6 161 85.6 2.68 4.32 1.35 9.3

Parvicardium pinnulatum

## SDM for: parvicardium_pinnulatum

Abiotic parameters

## McFadden's pseudo-R2 is: 0.34 
## Tjur's pseudo-R2 is: 0.26 
## Pearson's pseudo-R2 is: 0.31
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.95 2.796 -2.486 0.01291 *
om -3.917 2.201 -1.78 0.07515
gravel 0.3256 0.7491 0.4346 0.6639
silt 2.566 1.309 1.96 0.04998 *
clay -0.664 1.353 -0.4907 0.6237
arsenic -0.9137 3.273 -0.2792 0.7801
cadmium 0.6428 1.156 0.5562 0.5781
copper 2.425 1.777 1.365 0.1723
iron -1.007 2.109 -0.4775 0.633
manganese -2.056 2.701 -0.7615 0.4464
mercury -3.863 1.898 -2.035 0.04183 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.99 1.27 2.08 1.16 1.55 1.77 2.44 2.38 1.95 1.76

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2498 3082950 -0.0008102 0.9994
aquaculture -230.8 2785760 -8.285e-05 0.9999
city -2731 5314073 -0.0005139 0.9996
dredging_collect -1289 5058110 -0.0002549 0.9998
dredging_dump -3011 4212053 -0.0007149 0.9994
industry 175.5 684714 0.0002563 0.9998
shipping_mooring 1301 2289608 0.0005684 0.9995
shipping_traffic -306.1 1034133 -0.000296 0.9998
sewers_rain 2078 3222204 0.0006449 0.9995
sewers_waste -4166 5992940 -0.0006951 0.9994
wharves_city 5085 7705365 0.0006599 0.9995
wharves_industry 1573 6353257 0.0002475 0.9998
fisheries_trap 33.55 70070 0.0004789 0.9996
fisheries_trawl -31.83 157892 -0.0002016 0.9998
fisheries_net 51.54 6457820 7.982e-06 1
fisheries_dredge -372.6 1064505 -0.00035 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 61.5 153 121 101 9.81 58.8 27.1 93.6 148 228 142 3.81 1.15 1 22.5

Periploma leanum

## SDM for: periploma_leanum

Abiotic parameters

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.1 
## Pearson's pseudo-R2 is: 0.08
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -132.2 27635 -0.004783 0.9962
om -1.575 1.573 -1.001 0.3167
gravel -28.83 18298 -0.001576 0.9987
silt 1.055 1.528 0.6903 0.49
clay -647.1 148289 -0.004364 0.9965
arsenic 0.4741 0.8081 0.5867 0.5574
cadmium 1.38 1.58 0.8734 0.3824
copper 2.51 2.493 1.007 0.3139
iron -4.714 5.11 -0.9225 0.3563
manganese 0.7085 1.582 0.4478 0.6543
mercury 0.77 0.7267 1.06 0.2894 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.9 1 1.76 1 1.62 2.2 4.26 6.77 2.71 1.39

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -227.7 1501082 -0.0001517 0.9999
aquaculture -655.6 8111491 -8.083e-05 0.9999
city -665.8 7258664 -9.173e-05 0.9999
dredging_collect 164.9 11520200 1.431e-05 1
dredging_dump 426.6 5211430 8.186e-05 0.9999
industry 455.9 1040340 0.0004382 0.9997
shipping_mooring -171.4 6466121 -2.651e-05 1
shipping_traffic -37.4 2798229 -1.337e-05 1
sewers_rain 486.1 3734885 0.0001301 0.9999
sewers_waste -644.6 3762103 -0.0001713 0.9999
wharves_city 742.8 9029972 8.225e-05 0.9999
wharves_industry -1161 10311613 -0.0001126 0.9999
fisheries_trap 15.16 260244 5.824e-05 1
fisheries_trawl -171.4 2187840 -7.834e-05 0.9999
fisheries_net 11.81 290916 4.059e-05 1
fisheries_dredge -64.5 8989617 -7.175e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 86.8 63.7 117 58.7 15.1 78.3 32.9 40.1 43.2 80.9 113 1.49 15.9 1.15 28.8

Philine lima

## SDM for: philine_lima

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -157.4 448856 -0.0003506 0.9997
om -9.446 244648 -3.861e-05 1
gravel 1.884 549794 3.427e-06 1
silt 25.73 120585 0.0002134 0.9998
clay 3.612 2041819 1.769e-06 1
arsenic -99.38 527677 -0.0001883 0.9998
cadmium 46.64 389449 0.0001198 0.9999
copper 7.77 320737 2.423e-05 1
iron 5.156 458233 1.125e-05 1
manganese -96.32 760707 -0.0001266 0.9999
mercury -33.49 301617 -0.000111 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.49 2.61 2.07 1.42 1.71 5.8 3.83 4.91 6.15 2.97

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -132 261580 -0.0005048 0.9996
aquaculture 29.9 2012429 1.486e-05 1
city -44.81 2524621 -1.775e-05 1
dredging_collect -159.7 1088442 -0.0001467 0.9999
dredging_dump -24.51 2547376 -9.621e-06 1
industry -32.52 1014058 -3.207e-05 1
shipping_mooring -1.868 1479903 -1.262e-06 1
shipping_traffic -16.82 413635 -4.066e-05 1
sewers_rain -9.975 1818813 -5.484e-06 1
sewers_waste 47.74 3083842 1.548e-05 1
wharves_city 64.8 3296940 1.965e-05 1
wharves_industry 194.3 1348004 0.0001441 0.9999
fisheries_trap -166.4 673787 -0.0002469 0.9998
fisheries_trawl -108.3 748721 -0.0001446 0.9999
fisheries_net 5.202 183416 2.836e-05 1
fisheries_dredge 51.23 177084 0.0002893 0.9998 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 30.6 29.4 14.3 34.2 9.02 22.4 6.04 31.7 53.2 36.3 18.3 2.25 2.44 1.2 2.14

Philomedes sp

## SDM for: philomedes_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.18 
## Pearson's pseudo-R2 is: 0.17
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -330.6 87995 -0.003757 0.997
om 0.4032 1.74 0.2317 0.8168
gravel 0.2978 0.4143 0.7189 0.4722
silt -0.6917 1.525 -0.4535 0.6502
clay -1780 480174 -0.003706 0.997
arsenic 0.8166 1.255 0.6508 0.5152
cadmium -0.4933 1.402 -0.3518 0.725
copper -0.6786 1.526 -0.4448 0.6565
iron -0.4185 0.7183 -0.5827 0.5601
manganese 0.1148 2.364 0.04858 0.9613
mercury -1.455 2.229 -0.6528 0.5139 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.26 2.18 1 1.5 1.77 1.67 1.38 1.92 1.74

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -91.36 232639 -0.0003927 0.9997
aquaculture -414.9 2683553 -0.0001546 0.9999
city -194.1 3217121 -6.033e-05 1
dredging_collect -178.4 877096 -0.0002034 0.9998
dredging_dump -53.72 1830493 -2.935e-05 1
industry 196.9 620876 0.0003171 0.9997
shipping_mooring -321 1479941 -0.0002169 0.9998
shipping_traffic -17.55 607726 -2.888e-05 1
sewers_rain 68.12 3623940 1.88e-05 1
sewers_waste -107 4876340 -2.194e-05 1
wharves_city 354.4 3932131 9.014e-05 0.9999
wharves_industry 75.82 2040465 3.716e-05 1
fisheries_trap -63.41 409896 -0.0001547 0.9999
fisheries_trawl 5.201 116477 4.466e-05 1
fisheries_net 10.04 252488 3.976e-05 1
fisheries_dredge 30.14 179294 0.0001681 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 33.4 56.1 18.8 37 13.8 22.1 9.96 45.5 60.1 70.1 40.6 2.64 2.69 1.65 3.17

Pholoe longa

## SDM for: pholoe_longa

Abiotic parameters

## McFadden's pseudo-R2 is: -12.23 
## Tjur's pseudo-R2 is: -0.01 
## Pearson's pseudo-R2 is: 0
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.301e+15 7978909 -413766906 0 * * *
om -1.493e+15 14257309 -104685824 0 * * *
gravel 7.237e+14 8714549 83047230 0 * * *
silt 1.384e+15 15274819 90612828 0 * * *
clay -7.527e+14 21858102 -34434528 0 * * *
arsenic -1.752e+15 11951982 -146562517 0 * * *
cadmium 6.267e+14 10400421 60260861 0 * * *
copper 1.939e+15 13687347 141670271 0 * * *
iron -4.869e+14 9620115 -50615564 0 * * *
manganese -5.417e+14 14288707 -37912203 0 * * *
mercury -6.123e+14 12068606 -50738739 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -108.2 230852 -0.0004686 0.9996
aquaculture -372.7 7074900 -5.267e-05 1
city -426.4 3308637 -0.0001289 0.9999
dredging_collect -235.4 21696254 -1.085e-05 1
dredging_dump -82.52 6321537 -1.305e-05 1
industry 213.3 1182221 0.0001805 0.9999
shipping_mooring -280.9 11462006 -2.451e-05 1
shipping_traffic -98.85 3244147 -3.047e-05 1
sewers_rain 103.2 6422833 1.607e-05 1
sewers_waste -34.09 8749498 -3.896e-06 1
wharves_city 620.2 4779428 0.0001298 0.9999
wharves_industry 91.44 28604720 3.197e-06 1
fisheries_trap -14.2 464038 -3.06e-05 1
fisheries_trawl 11.79 560768 2.102e-05 1
fisheries_net 5.979 272327 2.195e-05 1
fisheries_dredge 40.26 6081530 6.621e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 58.6 47.9 255 80.8 13 103 34.1 46.3 61.4 78.8 312 9.76 3.81 1.77 36.1

Pholoe sp

## SDM for: pholoe_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.17 
## Tjur's pseudo-R2 is: 0.21 
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.4394 0.3545 -1.239 0.2152
om 0.8286 0.4832 1.715 0.08637
gravel -0.1573 0.2861 -0.55 0.5824
silt 0.01993 0.5099 0.03908 0.9688
clay -1.015 1.431 -0.7091 0.4782
arsenic -1.326 0.6602 -2.008 0.04465 *
cadmium -0.8395 0.4231 -1.984 0.04722 *
copper 0.5358 0.5223 1.026 0.305
iron -0.8158 0.4512 -1.808 0.0706
manganese 0.9999 0.5928 1.687 0.09163
mercury -0.4265 0.4402 -0.9688 0.3326 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.92 1.38 2.08 1.03 1.87 1.51 2.03 1.6 2.32 1.64

Influence indices

## McFadden's pseudo-R2 is: 0.25 
## Tjur's pseudo-R2 is: 0.3 
## Pearson's pseudo-R2 is: 0.3
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -56.59 6248 -0.009058 0.9928
aquaculture -2.655 2.519 -1.054 0.2919
city 2.949 2.544 1.159 0.2465
dredging_collect 2.485 2.047 1.214 0.2248
dredging_dump 5.83 2.541 2.294 0.02177 *
industry 0.6433 1.068 0.6023 0.547
shipping_mooring 0.5186 1.897 0.2734 0.7846
shipping_traffic 1.022 1.17 0.8732 0.3826
sewers_rain 5.001 2.62 1.909 0.05632
sewers_waste -8.576 3.618 -2.371 0.01776 *
wharves_city -4.4 2.936 -1.499 0.1339
wharves_industry -7.855 3.885 -2.022 0.04319 *
fisheries_trap 0.03443 0.2453 0.1404 0.8884
fisheries_trawl -1.74 1.004 -1.733 0.08307
fisheries_net -585.2 64787 -0.009033 0.9928
fisheries_dredge 1.384 1.235 1.121 0.2624 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 10.5 10 7.59 9.54 4.18 7.06 4.59 9.29 13.8 11.8 14.4 1.12 1.79 1 2.22

Phoxocephalus holbolli

## SDM for: phoxocephalus_holbolli

Abiotic parameters

## McFadden's pseudo-R2 is: 0.18 
## Tjur's pseudo-R2 is: 0.19 
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.719 0.4479 -3.837 0.0001243 * * *
om -0.81 0.6594 -1.228 0.2193
gravel -0.1437 0.3604 -0.3988 0.69
silt -0.1046 0.5294 -0.1976 0.8434
clay 0.6938 0.7053 0.9837 0.3253
arsenic -0.5911 1.013 -0.5835 0.5596
cadmium 0.6453 0.4406 1.465 0.143
copper -0.05452 0.6256 -0.08714 0.9306
iron -0.2543 0.6658 -0.3819 0.7025
manganese -0.3255 0.8668 -0.3756 0.7072
mercury -0.1344 0.6075 -0.2212 0.825 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.51 1.16 1.64 1.12 1.67 1.44 1.82 1.65 1.88 1.39

Influence indices

## McFadden's pseudo-R2 is: 0.5 
## Tjur's pseudo-R2 is: 0.48 
## Pearson's pseudo-R2 is: 0.47
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 66.08 7772 0.008502 0.9932
aquaculture -0.8315 4.123 -0.2017 0.8402
city 10.56 4.67 2.262 0.02369 *
dredging_collect 6.725 3.752 1.793 0.07305
dredging_dump 12.31 6.166 1.997 0.04581 *
industry -7.638 3.461 -2.207 0.02734 *
shipping_mooring 1.01 3.695 0.2734 0.7846
shipping_traffic 1.118 2.386 0.4687 0.6393
sewers_rain 13.34 6.982 1.91 0.05608
sewers_waste -22.85 11.26 -2.029 0.04245 *
wharves_city -17.13 6.755 -2.535 0.01124 *
wharves_industry -9.149 5.448 -1.679 0.09309
fisheries_trap 0.7861 0.3627 2.168 0.03019 *
fisheries_trawl -7.368 10.83 -0.6803 0.4963
fisheries_net 743.6 80587 0.009227 0.9926
fisheries_dredge -2.166 1.243 -1.743 0.08135 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.1 14.4 9.72 15.9 9.82 8.51 6.87 19.7 29.2 19.7 13.9 1.49 2.45 1 2.1

Polynoidae spp

## SDM for: polynoidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.24 
## Tjur's pseudo-R2 is: 0.26 
## Pearson's pseudo-R2 is: 0.25
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.339 0.4137 -3.237 0.00121 * *
om 2.01 0.6543 3.073 0.002121 * *
gravel -0.7474 0.4494 -1.663 0.09631
silt -0.661 0.5851 -1.13 0.2586
clay -0.07217 1.653 -0.04367 0.9652
arsenic -0.1377 0.6559 -0.2099 0.8338
cadmium -0.6335 0.5594 -1.133 0.2574
copper -0.544 0.684 -0.7953 0.4264
iron -0.8514 0.7809 -1.09 0.2756
manganese -0.163 0.726 -0.2245 0.8224
mercury 0.7763 0.5309 1.462 0.1437 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.29 1.2 2.05 1.05 1.8 1.76 2.38 2.18 2.43 1.86

Influence indices

## McFadden's pseudo-R2 is: 0.2 
## Tjur's pseudo-R2 is: 0.2 
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -73.68 8065 -0.009136 0.9927
aquaculture -2.528 2.749 -0.9195 0.3578
city -2.859 2.665 -1.073 0.2833
dredging_collect 3.45 2.226 1.55 0.1211
dredging_dump 1.889 2.39 0.7903 0.4293
industry 1.072 1.34 0.7998 0.4238
shipping_mooring 1.271 2.212 0.5748 0.5655
shipping_traffic 0.1962 1.092 0.1796 0.8574
sewers_rain 5.235 3.24 1.616 0.1061
sewers_waste -6.783 4.038 -1.68 0.09304
wharves_city 1.556 3.141 0.4954 0.6203
wharves_industry -7.486 4.055 -1.846 0.06484
fisheries_trap -2.591 1.874 -1.383 0.1668
fisheries_trawl -1.057 0.9408 -1.123 0.2614
fisheries_net -747.9 83622 -0.008943 0.9929
fisheries_dredge 0.2029 0.8197 0.2476 0.8045 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.1 8.87 7.51 8.28 4.93 8.5 3.89 11.3 14.9 10.3 13.6 1.29 2.06 1 1.66

Pontogeneia inermis

## SDM for: pontogeneia_inermis

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -113.7 251118 -0.0004526 0.9996
om -50.31 441711 -0.0001139 0.9999
gravel 32.36 153775 0.0002104 0.9998
silt 68.19 420624 0.0001621 0.9999
clay -9.711 506273 -1.918e-05 1
arsenic -45.88 1074917 -4.269e-05 1
cadmium -53.4 598912 -8.916e-05 0.9999
copper 99.55 669205 0.0001488 0.9999
iron 8.833 665738 1.327e-05 1
manganese -19 312188 -6.085e-05 1
mercury -6.325 479027 -1.32e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 5.05 1.52 5.08 1.51 7.72 7.08 8.96 6.4 4.43 8.2

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -132.4 255619 -0.000518 0.9996
aquaculture -264.7 2849030 -9.291e-05 0.9999
city -180.8 1027209 -0.000176 0.9999
dredging_collect -77.17 1149361 -6.715e-05 0.9999
dredging_dump 156.6 2889772 5.42e-05 1
industry 71.68 660587 0.0001085 0.9999
shipping_mooring -275.5 1058157 -0.0002604 0.9998
shipping_traffic -46.12 442675 -0.0001042 0.9999
sewers_rain 46.86 4431384 1.057e-05 1
sewers_waste -30.89 6090006 -5.073e-06 1
wharves_city 262.6 1727998 0.000152 0.9999
wharves_industry -70.36 2673518 -2.632e-05 1
fisheries_trap -189.2 430303 -0.0004398 0.9996
fisheries_trawl 11.99 407278 2.943e-05 1
fisheries_net 14.46 261862 5.523e-05 1
fisheries_dredge 0.9666 534177 1.809e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 24 15.4 15.2 40.7 9.2 11.4 5.49 44.4 59.3 28.7 35 1.66 3.52 1.71 2.89

Pontoporeia femorata

## SDM for: pontoporeia_femorata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.36 
## Tjur's pseudo-R2 is: 0.37 
## Pearson's pseudo-R2 is: 0.36
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -338 56324 -0.006001 0.9952
om 0.279 0.6352 0.4393 0.6604
gravel 0.1841 0.5257 0.3501 0.7262
silt 0.893 0.855 1.044 0.2963
clay -1835 307350 -0.00597 0.9952
arsenic -1.69 0.8365 -2.02 0.04338 *
cadmium 0.4184 0.4318 0.9691 0.3325
copper 0.6055 0.5924 1.022 0.3067
iron 0.04479 0.6532 0.06857 0.9453
manganese 1.063 0.6523 1.629 0.1032
mercury 0.02013 0.5123 0.0393 0.9687 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.86 1.41 2.14 1 1.82 1.24 1.69 1.5 1.64 1.43

Influence indices

## McFadden's pseudo-R2 is: 0.41 
## Tjur's pseudo-R2 is: 0.42 
## Pearson's pseudo-R2 is: 0.44
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -255.5 81485 -0.003136 0.9975
aquaculture 0.5583 4.602 0.1213 0.9034
city -1.779 2.748 -0.6475 0.5173
dredging_collect -9.96 6.483 -1.536 0.1245
dredging_dump 2.715 2.717 0.999 0.3178
industry -5.829 3.439 -1.695 0.09006
shipping_mooring 1.908 2.367 0.8059 0.4203
shipping_traffic 2.658 2.88 0.923 0.356
sewers_rain 0.5592 3.663 0.1527 0.8787
sewers_waste -0.7551 5.062 -0.1492 0.8814
wharves_city 0.4469 3.481 0.1284 0.8979
wharves_industry 9.532 7.268 1.312 0.1897
fisheries_trap 0.3215 0.2973 1.081 0.2795
fisheries_trawl -646.4 242747 -0.002663 0.9979
fisheries_net -755.9 483832 -0.001562 0.9988
fisheries_dredge -22.76 53107 -0.0004286 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 8.18 8.85 17.1 7.51 10.4 3.05 8.43 6.2 7.39 12 19.9 1.35 1 1 1

Praxillella praetermissa

## SDM for: praxillella_praetermissa

Abiotic parameters

## McFadden's pseudo-R2 is: 0.22 
## Tjur's pseudo-R2 is: 0.18 
## Pearson's pseudo-R2 is: 0.18
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.4 1162 -0.008951 0.9929
om -0.09507 0.6811 -0.1396 0.889
gravel -27.89 4150 -0.006722 0.9946
silt 0.3509 0.9224 0.3804 0.7036
clay -1.636 5.059 -0.3234 0.7464
arsenic 0.2253 0.4521 0.4984 0.6182
cadmium 0.07706 0.6023 0.128 0.8982
copper 0.3128 0.9471 0.3303 0.7412
iron -0.9361 1.692 -0.5532 0.5801
manganese 0.8187 0.7688 1.065 0.2869
mercury 0.2264 0.5489 0.4124 0.6801 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.83 1 1.98 1.01 1.55 1.35 2.48 3.24 2.04 1.39

Influence indices

## McFadden's pseudo-R2 is: 0.53 
## Tjur's pseudo-R2 is: 0.45 
## Pearson's pseudo-R2 is: 0.43
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -69.75 10367 -0.006728 0.9946
aquaculture 11.62 7.473 1.555 0.12
city -24.97 25.45 -0.9812 0.3265
dredging_collect -10.97 10.81 -1.015 0.3103
dredging_dump -5.416 6.608 -0.8197 0.4124
industry -2.359 3.242 -0.7278 0.4667
shipping_mooring 17.37 11.77 1.476 0.14
shipping_traffic 4.75 4.314 1.101 0.2708
sewers_rain -3.62 5.494 -0.6589 0.51
sewers_waste -0.9263 10.55 -0.08779 0.93
wharves_city 17.38 20.17 0.8616 0.3889
wharves_industry 15.85 12.15 1.305 0.192
fisheries_trap 0.8655 2.4 0.3606 0.7184
fisheries_trawl -58.94 62.08 -0.9494 0.3424
fisheries_net -482.6 107495 -0.00449 0.9964
fisheries_dredge 5.239 3.934 1.332 0.183 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.9 41.9 15.8 9.85 3.8 17 7.06 9.09 18.4 36.3 18.3 1.59 1.55 1 3.47

Propebela turricula

## SDM for: propebela_turricula

Abiotic parameters

## McFadden's pseudo-R2 is: 0.16 
## Tjur's pseudo-R2 is: 0.05 
## Pearson's pseudo-R2 is: 0.04
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -127.3 23088 -0.005513 0.9956
om 0.4587 1.11 0.4133 0.6794
gravel -32.1 18934 -0.001695 0.9986
silt -0.2984 1.114 -0.2679 0.7888
clay -629.1 122636 -0.00513 0.9959
arsenic 0.03976 1.277 0.03113 0.9752
cadmium -0.8077 0.9998 -0.8078 0.4192
copper 0.1158 1.323 0.08747 0.9303
iron -1.621 1.957 -0.8286 0.4073
manganese 1.113 1.688 0.6596 0.5095
mercury -0.1672 1.129 -0.148 0.8823 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.99 1 2.01 1 1.64 1.61 2.22 2.74 2.96 1.98

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -378.6 994430 -0.0003807 0.9997
aquaculture 959.8 2613836 0.0003672 0.9997
city -890.5 10801018 -8.245e-05 0.9999
dredging_collect 524.7 1503707 0.0003489 0.9997
dredging_dump 809.3 4047658 2e-04 0.9998
industry -311.4 2132905 -0.000146 0.9999
shipping_mooring 375 3907328 9.597e-05 0.9999
shipping_traffic -153.9 1238146 -0.0001243 0.9999
sewers_rain 232.3 2590747 8.966e-05 0.9999
sewers_waste 653.7 6119282 0.0001068 0.9999
wharves_city 408.6 9943058 4.11e-05 1
wharves_industry -1388 5004811 -0.0002774 0.9998
fisheries_trap 3.015 142198 2.12e-05 1
fisheries_trawl -3.921 198222 -1.978e-05 1
fisheries_net -15.48 6457684 -2.396e-06 1
fisheries_dredge 31.93 263961 0.0001209 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 36.4 86.5 23.4 53.4 28 48.4 19.8 49.2 105 83.5 74.5 1.65 3.79 1 3.4

Protomedeia fasciata

## SDM for: protomedeia_fasciata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.31 
## Tjur's pseudo-R2 is: 0.22 
## Pearson's pseudo-R2 is: 0.21
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -286.4 49594 -0.005774 0.9954
om -0.9114 0.8312 -1.096 0.2729
gravel 1.222 0.6627 1.844 0.06519
silt 2.751 1.511 1.821 0.06863
clay -1540 270625 -0.005689 0.9955
arsenic -4.199 2.57 -1.634 0.1023
cadmium 0.1073 0.745 0.1441 0.8854
copper 0.8999 0.9298 0.9678 0.3332
iron -0.07938 1.108 -0.07165 0.9429
manganese 2.454 1.641 1.495 0.1348
mercury -1.888 1.306 -1.445 0.1484 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.99 2.61 3.27 1 2.52 1.63 1.84 1.89 3.98 3.22

Influence indices

## McFadden's pseudo-R2 is: 0.62 
## Tjur's pseudo-R2 is: 0.56 
## Pearson's pseudo-R2 is: 0.58
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -30.43 42.77 -0.7116 0.4767
aquaculture -33.98 30.52 -1.113 0.2656
city -17.06 12.63 -1.351 0.1768
dredging_collect -29.37 23.21 -1.266 0.2056
dredging_dump 107.7 84.2 1.279 0.201
industry -27.26 21.36 -1.276 0.2018
shipping_mooring -83.28 64.75 -1.286 0.1984
shipping_traffic -17.08 13.59 -1.257 0.2087
sewers_rain -25.16 23.1 -1.089 0.2761
sewers_waste 26.29 30.99 0.8485 0.3962
wharves_city 10.62 12.42 0.8548 0.3927
wharves_industry -3.717 14.13 -0.2631 0.7925
fisheries_trap -1.81 1.412 -1.282 0.1999
fisheries_trawl 3.466 5.639 0.6146 0.5388
fisheries_net 5.945 379.9 0.01565 0.9875
fisheries_dredge -6.132 5.275 -1.162 0.2451 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 57.6 25.7 42 144 25.7 115 16.5 33.3 52.3 27.3 23.8 3.99 9.89 1 4.96

Protomedeia grandimana

## SDM for: protomedeia_grandimana

Abiotic parameters

## McFadden's pseudo-R2 is: 0.33 
## Tjur's pseudo-R2 is: 0.37 
## Pearson's pseudo-R2 is: 0.36
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.191 2.245 -0.5303 0.5959
om 2.011 0.6965 2.887 0.003884 * *
gravel -0.04722 0.3597 -0.1313 0.8956
silt -0.8222 0.6085 -1.351 0.1766
clay -6.299 12.26 -0.5137 0.6074
arsenic -1.32 0.8339 -1.583 0.1133
cadmium -1.239 0.619 -2.002 0.04532 *
copper 0.5195 0.6455 0.8048 0.4209
iron -1.046 0.7607 -1.375 0.1691
manganese 0.7802 0.803 0.9715 0.3313
mercury 1.809 0.7619 2.375 0.01756 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.22 1.18 2.21 1.04 2.31 1.99 2.34 2.09 2.37 1.97

Influence indices

## McFadden's pseudo-R2 is: 0.22 
## Tjur's pseudo-R2 is: 0.27 
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -63.73 6192 -0.01029 0.9918
aquaculture -1.913 2.285 -0.8374 0.4024
city -3.533 2.486 -1.421 0.1554
dredging_collect 1.517 1.78 0.8524 0.394
dredging_dump 5.225 2.127 2.457 0.01402 *
industry 0.7829 1.061 0.7376 0.4608
shipping_mooring 1.015 1.879 0.5399 0.5893
shipping_traffic -0.885 0.9425 -0.939 0.3477
sewers_rain 4.904 2.838 1.728 0.08403
sewers_waste -5.241 3.599 -1.456 0.1453
wharves_city 2.582 2.771 0.9319 0.3514
wharves_industry -7.184 3.205 -2.241 0.02501 *
fisheries_trap -0.3069 0.2535 -1.211 0.226
fisheries_trawl -0.2174 0.4308 -0.5045 0.6139
fisheries_net -660 64201 -0.01028 0.9918
fisheries_dredge 0.4733 0.6665 0.7101 0.4777 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.65 9.7 6.66 7.94 4.13 7.34 3.74 10.9 14.5 10.6 12.1 1.16 1.44 1 1.7

Puncturella noachina

## SDM for: puncturella_noachina

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -92.72 150483 -0.0006162 0.9995
om -51.01 168846 -0.0003021 0.9998
gravel 26.9 70521 0.0003815 0.9997
silt 22.7 175221 0.0001296 0.9999
clay 2.983 256019 1.165e-05 1
arsenic -38.2 199649 -0.0001913 0.9998
cadmium 29.85 97550 0.000306 0.9998
copper 30.54 185178 0.0001649 0.9999
iron 7.023 449909 1.561e-05 1
manganese -28.08 250267 -0.0001122 0.9999
mercury 17.88 178947 9.994e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.01 3.95 3.8 1.1 2.74 2.8 4.52 5.78 4.55 3.87

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -67.54 140643 -0.0004802 0.9996
aquaculture 71.34 4842619 1.473e-05 1
city 145.3 10402474 1.397e-05 1
dredging_collect 77.29 774144 9.983e-05 0.9999
dredging_dump -69.13 2155231 -3.207e-05 1
industry 46.71 2178614 2.144e-05 1
shipping_mooring 71.66 2724611 2.63e-05 1
shipping_traffic 44.96 2905192 1.548e-05 1
sewers_rain -7.165 4554958 -1.573e-06 1
sewers_waste 13.34 2622642 5.087e-06 1
wharves_city -175.1 10700829 -1.637e-05 1
wharves_industry -93.09 1857286 -5.012e-05 1
fisheries_trap 6.767 578628 1.17e-05 1
fisheries_trawl -4.491 150280 -2.989e-05 1
fisheries_net 2.735 404568 6.76e-06 1
fisheries_dredge -8.065 912207 -8.842e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 78.2 177 16.8 40.9 43.9 52.4 45.4 67.8 41.7 170 36.4 8.18 4.68 4.35 5.38

Quasimelita formosa

## SDM for: quasimelita_formosa

Abiotic parameters

## McFadden's pseudo-R2 is: 0.15 
## Tjur's pseudo-R2 is: 0.14 
## Pearson's pseudo-R2 is: 0.14
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.692 0.5949 -2.845 0.004448 * *
om 0.7093 0.5913 1.2 0.2303
gravel 0.05127 0.3645 0.1407 0.8881
silt 0.4947 0.6072 0.8147 0.4152
clay -1.495 2.816 -0.5309 0.5955
arsenic -0.04478 0.4996 -0.08963 0.9286
cadmium -0.7294 0.6122 -1.191 0.2335
copper 0.615 0.6009 1.024 0.3061
iron -0.132 0.402 -0.3282 0.7427
manganese -1.496 0.743 -2.014 0.04402 *
mercury 0.1356 0.4894 0.2771 0.7817 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.08 1.21 2.09 1.05 1.57 2.07 2.13 1.46 1.99 1.58

Influence indices

## McFadden's pseudo-R2 is: 0.26 
## Tjur's pseudo-R2 is: 0.26 
## Pearson's pseudo-R2 is: 0.26
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.37 0.8102 -2.925 0.003443 * *
aquaculture 7.495 4.728 1.585 0.1129
city -1.155 3.494 -0.3305 0.741
dredging_collect -0.99 2.972 -0.3331 0.7391
dredging_dump -5.633 3.499 -1.61 0.1075
industry 1.128 1.59 0.7092 0.4782
shipping_mooring 2.32 3.006 0.7717 0.4403
shipping_traffic 1.653 1.243 1.33 0.1835
sewers_rain -4.325 3.371 -1.283 0.1994
sewers_waste 9.765 5.882 1.66 0.0969
wharves_city 4.436 5.124 0.8658 0.3866
wharves_industry 0.3785 4.248 0.0891 0.929
fisheries_trap -0.2699 0.3282 -0.8223 0.4109
fisheries_trawl -0.3117 0.3512 -0.8877 0.3747
fisheries_net 0.7357 3.53 0.2084 0.8349
fisheries_dredge 0.2131 1.242 0.1716 0.8638 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 14.8 12.2 9.66 11.4 5.11 9.86 4.19 11.5 19.4 17.1 13.6 1.18 1.46 1 2.35

Quasimelita quadrispinosa

## SDM for: quasimelita_quadrispinosa

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -164.2 390466 -0.0004205 0.9997
om 123.5 326201 0.0003785 0.9997
gravel -37.77 359356 -0.0001051 0.9999
silt -77.04 770127 -1e-04 0.9999
clay 98.65 686832 0.0001436 0.9999
arsenic -142.2 711751 -0.0001998 0.9998
cadmium -27.17 605434 -4.488e-05 1
copper 97.63 388985 0.000251 0.9998
iron -116.7 737376 -0.0001583 0.9999
manganese 37.35 552277 6.762e-05 0.9999
mercury 30.18 251967 0.0001198 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.82 2.89 9.81 1.1 5.57 9.06 5.56 8.88 6.16 3.33

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -90.81 346796 -0.0002618 0.9998
aquaculture 337.7 14867673 2.271e-05 1
city 74.87 20706774 3.616e-06 1
dredging_collect 226.6 6417189 3.531e-05 1
dredging_dump 56.87 5003265 1.137e-05 1
industry -175.2 1904624 -9.2e-05 0.9999
shipping_mooring 293.7 8995786 3.265e-05 1
shipping_traffic 66.97 5979316 1.12e-05 1
sewers_rain -15.88 2149956 -7.387e-06 1
sewers_waste 25.65 7702137 3.331e-06 1
wharves_city -192.5 20119042 -9.568e-06 1
wharves_industry -235.9 2676196 -8.815e-05 0.9999
fisheries_trap -7.447 345469 -2.156e-05 1
fisheries_trawl -15.2 1249359 -1.216e-05 1
fisheries_net -3.215 223978 -1.435e-05 1
fisheries_dredge 1.873 1597431 1.173e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 125 305 57.9 55.2 10.8 85.1 55.5 18.4 63.7 330 24.6 2.21 12.2 1.46 9.59

Retusa obtusa

## SDM for: retusa_obtusa

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -156.8 256927 -0.0006101 0.9995
om -39.64 337208 -0.0001176 0.9999
gravel 22.06 109124 0.0002021 0.9998
silt -1.387 248244 -5.588e-06 1
clay 27.6 382853 7.209e-05 0.9999
arsenic -161.7 378101 -0.0004276 0.9997
cadmium 42.35 104673 0.0004046 0.9997
copper 79.79 178804 0.0004462 0.9996
iron 30.12 88462 0.0003405 0.9997
manganese 4.628 114437 4.044e-05 1
mercury -16.22 189061 -8.579e-05 0.9999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 4.08 1.31 2.78 1.17 2.72 2.1 2.86 2.14 1.53 2.52

Influence indices

## McFadden's pseudo-R2 is: -5.68 
## Tjur's pseudo-R2 is: 0.49 
## Pearson's pseudo-R2 is: 0.31
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.698e+15 7496963 -359928090 0 * * *
aquaculture 1.324e+15 66771996 19833430 0 * * *
city 1.184e+15 60515998 19565080 0 * * *
dredging_collect 2.363e+15 47886179 49336796 0 * * *
dredging_dump 6.956e+15 55589796 125128640 0 * * *
industry -2.714e+15 30193191 -89885547 0 * * *
shipping_mooring -3.942e+14 51224704 -7696358 0 * * *
shipping_traffic -1.019e+15 22815885 -44683654 0 * * *
sewers_rain 2.056e+15 67164046 30618588 0 * * *
sewers_waste -2.785e+15 90359677 -30820178 0 * * *
wharves_city -2.962e+15 72465526 -40880797 0 * * *
wharves_industry -4.323e+15 79067645 -54675006 0 * * *
fisheries_trap 9.826e+13 7277539 13502424 0 * * *
fisheries_trawl 3.331e+14 8821984 37763066 0 * * *
fisheries_net -1.628e+14 7219163 -22544924 0 * * *
fisheries_dredge -6.887e+14 19454028 -35400346 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Sabellidae spp

## SDM for: sabellidae_spp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.32 
## Tjur's pseudo-R2 is: 0.29 
## Pearson's pseudo-R2 is: 0.28
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.62 1174 -0.009047 0.9928
om 0.201 0.7698 0.261 0.7941
gravel -29.67 4191 -0.007078 0.9944
silt -0.7105 0.9274 -0.7661 0.4436
clay 1.382 0.8095 1.707 0.08782
arsenic -1.324 1.148 -1.153 0.249
cadmium 1.347 0.6348 2.122 0.0338 *
copper -0.211 0.9202 -0.2293 0.8186
iron -0.3527 1.546 -0.2282 0.8195
manganese 0.9739 0.8142 1.196 0.2317
mercury 0.5238 0.5284 0.9914 0.3215 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.01 1 2.15 1.25 1.8 1.3 2.27 2.69 1.95 1.38

Influence indices

## McFadden's pseudo-R2 is: 0.3 
## Tjur's pseudo-R2 is: 0.25 
## Pearson's pseudo-R2 is: 0.25
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.965 1.442 -2.75 0.005957 * *
aquaculture -1.386 3.387 -0.4091 0.6825
city -0.01064 3.683 -0.002889 0.9977
dredging_collect 4.399 4.341 1.013 0.311
dredging_dump 3.071 3.483 0.8817 0.3779
industry 3.852 2.634 1.462 0.1436
shipping_mooring 3.234 2.87 1.127 0.2599
shipping_traffic -2.907 3.062 -0.9495 0.3424
sewers_rain 8.219 5.193 1.583 0.1135
sewers_waste -8.36 5.926 -1.411 0.1583
wharves_city -3.732 3.934 -0.9485 0.3429
wharves_industry -9.842 7.119 -1.382 0.1668
fisheries_trap 1.351 0.6097 2.217 0.02664 *
fisheries_trawl -1.344 4.293 -0.313 0.7543
fisheries_net -0.5748 2.692 -0.2135 0.8309
fisheries_dredge -2.504 2.829 -0.8851 0.3761 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 8.07 7.99 10.2 8.57 7.5 5.89 8.29 12.2 12.9 8.8 17.3 2.74 1.21 1 2.14

Scoletoma fragilis

## SDM for: scoletoma_fragilis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.59 
## Tjur's pseudo-R2 is: 0.44 
## Pearson's pseudo-R2 is: 0.42
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -22.65 2240 -0.01011 0.9919
om -0.4338 2.097 -0.2069 0.8361
gravel -12.98 7996 -0.001623 0.9987
silt 10.33 7.262 1.423 0.1549
clay 2.615 2.168 1.206 0.2277
arsenic -10.18 6.309 -1.613 0.1068
cadmium 1.243 2.017 0.616 0.5379
copper 14.27 8.768 1.627 0.1036
iron -13.63 8.743 -1.559 0.119
manganese 11.01 6.346 1.734 0.08288
mercury -9.345 5.604 -1.668 0.09536 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 3.57 1 7.89 1.54 7.5 1.89 11.5 7.98 9.08 7.2

Influence indices

## McFadden's pseudo-R2 is: 0.58 
## Tjur's pseudo-R2 is: 0.47 
## Pearson's pseudo-R2 is: 0.48
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -55.49 486.2 -0.1141 0.9091
aquaculture -55.82 42.77 -1.305 0.1919
city -148.3 93.87 -1.579 0.1142
dredging_collect 114.9 65.52 1.754 0.07942
dredging_dump 54.64 38.63 1.414 0.1573
industry 5.48 11.27 0.4863 0.6267
shipping_mooring 32.22 27.61 1.167 0.2433
shipping_traffic -32.15 21.51 -1.495 0.1349
sewers_rain 88.02 56.16 1.567 0.1171
sewers_waste -87.79 63.52 -1.382 0.1669
wharves_city 116.1 75.24 1.543 0.1229
wharves_industry -151.3 88.18 -1.715 0.08625
fisheries_trap -18.28 12.09 -1.512 0.1306
fisheries_trawl 12.49 1268 0.00985 0.9921
fisheries_net 0.4484 3.871 0.1158 0.9078
fisheries_dredge 2.826 1054 0.002682 0.9979 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 35 115 55.4 35 11.5 14.4 19.7 59.4 43.2 107 75.3 3.58 1 1.02 1

Scoletoma sp

## SDM for: scoletoma_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.66 
## Tjur's pseudo-R2 is: 0.5 
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -42.03 11416 -0.003682 0.9971
om -16.62 19.01 -0.8743 0.382
gravel -24.58 14777 -0.001663 0.9987
silt 5.955 7.944 0.7497 0.4535
clay -59.72 59340 -0.001006 0.9992
arsenic 6.132 11.91 0.5147 0.6067
cadmium -4.616 5.231 -0.8825 0.3775
copper 9.748 13 0.75 0.4532
iron 2.445 3.681 0.6642 0.5065
manganese -14.07 25.76 -0.5463 0.5848
mercury -10.48 14.47 -0.7241 0.469 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 6.19 1 4 1 6.04 3.76 4.79 5.58 7.31 4.96

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -52.36 143714 -0.0003644 0.9997
aquaculture -104.1 2038169 -5.109e-05 1
city 35.37 2940960 1.203e-05 1
dredging_collect -18.11 3776616 -4.796e-06 1
dredging_dump -33.46 2851582 -1.173e-05 1
industry 80.68 479017 0.0001684 0.9999
shipping_mooring -46.46 3580537 -1.298e-05 1
shipping_traffic -11.27 681163 -1.654e-05 1
sewers_rain 60.22 3202956 1.88e-05 1
sewers_waste -82.7 4527545 -1.827e-05 1
wharves_city -12.23 4788895 -2.553e-06 1
wharves_industry -0.6252 5946143 -1.052e-07 1
fisheries_trap 12.03 158705 7.582e-05 0.9999
fisheries_trawl 10.99 371397 2.959e-05 1
fisheries_net 3.301 126294 2.613e-05 1
fisheries_dredge 11.44 437581 2.614e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 28.8 36 56.7 42.9 9.56 38.8 10.9 42.9 57.6 66.6 89.9 4.06 2.62 1.34 4.41

Scoloplos sp

## SDM for: scoloplos_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Serripes groenlandicus

## SDM for: serripes_groenlandicus

Abiotic parameters

## McFadden's pseudo-R2 is: -2.79 
## Tjur's pseudo-R2 is: 0.5 
## Pearson's pseudo-R2 is: 0.49
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.57e+15 7978909 -196769587 0 * * *
om -3.245e+14 14257309 -22760563 0 * * *
gravel 2.134e+14 8714549 24491989 0 * * *
silt -5.273e+13 15274819 -3451885 0 * * *
clay -1.581e+15 21858102 -72321094 0 * * *
arsenic -6.07e+13 11951982 -5078852 0 * * *
cadmium 1.173e+14 10400421 11282206 0 * * *
copper 3.939e+14 13687347 28778234 0 * * *
iron -1.633e+14 9620115 -16972757 0 * * *
manganese -4.656e+13 14288707 -3258821 0 * * *
mercury 1.036e+14 12068606 8584835 0 * * * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -559.1 6362172 -8.787e-05 0.9999
aquaculture 699.9 31262734 2.239e-05 1
city 635.2 33661978 1.887e-05 1
dredging_collect -664.6 39738271 -1.673e-05 1
dredging_dump -663.3 29581682 -2.242e-05 1
industry 135.3 13310113 1.017e-05 1
shipping_mooring 540.3 31201517 1.732e-05 1
shipping_traffic -460.3 16566389 -2.778e-05 1
sewers_rain -912.6 34642702 -2.634e-05 1
sewers_waste 879.3 40975925 2.146e-05 1
wharves_city -639.6 25283511 -2.53e-05 1
wharves_industry 1887 64272730 2.937e-05 1
fisheries_trap -48.53 7046970 -6.887e-06 1
fisheries_trawl 135.1 2782398 4.857e-05 1
fisheries_net 140.8 7061711 1.994e-05 1
fisheries_dredge -73.28 19129317 -3.831e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 421 103 163 122 130 350 162 50.2 185 20.2 232 1.11 118 1.09 1.78

Sipuncula

## SDM for: sipuncula

Abiotic parameters

## McFadden's pseudo-R2 is: 0.2 
## Tjur's pseudo-R2 is: 0.14 
## Pearson's pseudo-R2 is: 0.13
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.04 1228 -0.008987 0.9928
om -0.06194 0.6334 -0.09778 0.9221
gravel -30.31 4384 -0.006913 0.9945
silt 1.606 0.9186 1.748 0.08043
clay -0.9246 1.502 -0.6157 0.5381
arsenic -0.215 0.4955 -0.4339 0.6644
cadmium -0.003982 0.6238 -0.006384 0.9949
copper 0.9447 0.9321 1.014 0.3108
iron -1.095 1.373 -0.797 0.4254
manganese 0.02073 0.7768 0.02669 0.9787
mercury -0.753 0.7264 -1.037 0.2999 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.89 1 1.96 1.04 1.62 1.64 2.88 3.12 2.14 1.7

Influence indices

## McFadden's pseudo-R2 is: 0.19 
## Tjur's pseudo-R2 is: 0.17 
## Pearson's pseudo-R2 is: 0.19
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -63.64 9900 -0.006429 0.9949
aquaculture -1.972 3.528 -0.5591 0.5761
city -5.898 5.934 -0.9939 0.3203
dredging_collect 0.5101 2.952 0.1728 0.8628
dredging_dump 3.151 3.309 0.9524 0.3409
industry 2.518 1.567 1.607 0.108
shipping_mooring 1.088 3.567 0.3051 0.7603
shipping_traffic -1.256 1.784 -0.7041 0.4814
sewers_rain 4.471 3.685 1.213 0.225
sewers_waste -4.955 4.74 -1.045 0.2959
wharves_city 4.911 5.873 0.8362 0.403
wharves_industry -5.526 5.165 -1.07 0.2847
fisheries_trap 0.1574 0.3524 0.4468 0.655
fisheries_trawl -1.243 1.586 -0.784 0.433
fisheries_net -634.2 102651 -0.006178 0.9951
fisheries_dredge -0.143 0.9339 -0.1531 0.8783 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 11.3 14.3 6.86 8.03 3.17 11.1 4.93 11.3 15.3 15 12.3 1.24 1.5 1 1.72

Solamen glandula

## SDM for: solamen_glandula

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Solariella sp

## SDM for: solariella_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0.63 
## Tjur's pseudo-R2 is: 0.57 
## Pearson's pseudo-R2 is: 0.56
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -15.78 8.372 -1.885 0.0594
om -2.211 1.785 -1.238 0.2156
gravel 1.236 0.8289 1.491 0.1358
silt 4.686 2.543 1.843 0.06532
clay -20.23 17.58 -1.151 0.2498
arsenic 2.721 3.381 0.8049 0.4209
cadmium -2.661 1.976 -1.347 0.1781
copper 1.346 1.635 0.8232 0.4104
iron -0.8998 2.432 -0.37 0.7114
manganese -6.382 7.393 -0.8632 0.388
mercury -8.379 4.247 -1.973 0.04852 * 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.82 1.39 4.1 1.34 1.55 2.01 1.42 2.21 2.72 3.44

Influence indices

## McFadden's pseudo-R2 is: 0.61 
## Tjur's pseudo-R2 is: 0.52 
## Pearson's pseudo-R2 is: 0.5
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -133.2 14038 -0.00949 0.9924
aquaculture -44.82 27.08 -1.655 0.09795
city -200.2 198.8 -1.007 0.3138
dredging_collect 3.86 13.36 0.2889 0.7726
dredging_dump -20.27 42.16 -0.4808 0.6307
industry -7.981 13.5 -0.5911 0.5545
shipping_mooring -24.27 32.46 -0.7474 0.4548
shipping_traffic -9.323 8.823 -1.057 0.2906
sewers_rain 60.97 45.29 1.346 0.1782
sewers_waste -112.6 67.14 -1.678 0.09338
wharves_city 226.7 223.8 1.013 0.311
wharves_industry 13.36 21.06 0.6342 0.526
fisheries_trap -2.575 3.026 -0.851 0.3948
fisheries_trawl 3.667 3.425 1.071 0.2843
fisheries_net -679.2 145560 -0.004666 0.9963
fisheries_dredge -3.624 2.652 -1.366 0.1719 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 25.8 334 27.5 72.6 21.1 37.4 15.6 53.8 63.3 387 37.8 2.32 6.36 1 3.33

Strongylocentrotus sp

## SDM for: strongylocentrotus_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -253.2 569872 -0.0004443 0.9996
om -146 346106 -0.0004218 0.9997
gravel 27.65 96054 0.0002879 0.9998
silt 119.1 306874 0.000388 0.9997
clay -377.1 1959930 -0.0001924 0.9998
arsenic 25.65 237382 0.000108 0.9999
cadmium -55.59 190996 -0.0002911 0.9998
copper 63.14 497712 0.0001269 0.9999
iron 12.9 108119 0.0001193 0.9999
manganese 0.1801 556386 3.236e-07 1
mercury -132 399118 -0.0003308 0.9997 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.19 2.78 5.98 2.23 3.11 3.52 9.64 2.4 8.71 4.38

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -48.06 75858 -0.0006335 0.9995
aquaculture -18.14 897599 -2.021e-05 1
city -36.02 627166 -5.743e-05 1
dredging_collect -9.82 404726 -2.426e-05 1
dredging_dump -7.461 459611 -1.623e-05 1
industry -2.274 398214 -5.711e-06 1
shipping_mooring -50.52 538479 -9.381e-05 0.9999
shipping_traffic 7.258 183375 3.958e-05 1
sewers_rain -81.01 789813 -0.0001026 0.9999
sewers_waste 100.1 1060232 9.438e-05 0.9999
wharves_city 57.74 826509 6.986e-05 0.9999
wharves_industry 14.73 655559 2.247e-05 1
fisheries_trap -22.82 296552 -7.695e-05 0.9999
fisheries_trawl 10.73 50677 0.0002118 0.9998
fisheries_net 6.716 74033 9.072e-05 0.9999
fisheries_dredge -3.998 166667 -2.399e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 18.6 18.4 12.4 14.6 11.3 13.7 3.57 11.2 18.5 23.9 18.8 2.32 2.85 1.31 1.92

Tachyrhynchus erosus

## SDM for: tachyrhynchus_erosus

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Thracia septentrionalis

## SDM for: thracia_septentrionalis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.13 
## Tjur's pseudo-R2 is: 0.14 
## Pearson's pseudo-R2 is: 0.16
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.713 0.4005 -4.277 1.896e-05 * * *
om -0.8216 0.6204 -1.324 0.1854
gravel 0.1418 0.3199 0.4434 0.6575
silt 0.3665 0.5504 0.6659 0.5055
clay 0.2645 0.6859 0.3856 0.6998
arsenic -0.03919 0.6671 -0.05874 0.9532
cadmium 0.2923 0.411 0.7113 0.4769
copper -0.02557 0.6218 -0.04112 0.9672
iron -0.3614 0.6353 -0.5689 0.5694
manganese 0.1917 0.7576 0.253 0.8003
mercury -0.9035 0.7035 -1.284 0.199 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.61 1.2 1.81 1.13 1.6 1.41 1.94 1.68 1.97 1.49

Influence indices

## McFadden's pseudo-R2 is: 0.25 
## Tjur's pseudo-R2 is: 0.26 
## Pearson's pseudo-R2 is: 0.27
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -54.1 5800 -0.009328 0.9926
aquaculture 9.15 4.447 2.058 0.03962 *
city 11.16 4.222 2.642 0.008238 * *
dredging_collect -0.2449 3.055 -0.08017 0.9361
dredging_dump -3.418 2.791 -1.224 0.2208
industry -0.8043 1.38 -0.5827 0.5601
shipping_mooring 5.303 2.869 1.848 0.06458
shipping_traffic 1.733 1.282 1.352 0.1765
sewers_rain -4.03 3.679 -1.095 0.2733
sewers_waste 4.944 5.029 0.9832 0.3255
wharves_city -12.05 4.717 -2.555 0.01061 *
wharves_industry 2.788 4.696 0.5936 0.5528
fisheries_trap 0.726 0.3127 2.321 0.02026 *
fisheries_trawl -0.2674 0.3331 -0.8029 0.422
fisheries_net -536.4 60137 -0.008919 0.9929
fisheries_dredge -2.448 1.672 -1.464 0.1433 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 15.1 13.8 10 8.97 4.97 9.03 4.52 11.3 16.3 14.4 15.5 1.37 1.47 1 2.55

Thyasira gouldi

## SDM for: thyasira_gouldi

Abiotic parameters

## McFadden's pseudo-R2 is: 0.19 
## Tjur's pseudo-R2 is: 0.21 
## Pearson's pseudo-R2 is: 0.2
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.937 0.972 -1.993 0.04627 *
om 1.1 0.5836 1.884 0.05954
gravel -0.2151 0.3224 -0.667 0.5048
silt -0.09162 0.5824 -0.1573 0.875
clay -4.1 5.058 -0.8106 0.4176
arsenic 0.1166 0.5209 0.2237 0.823
cadmium -1.434 0.6438 -2.227 0.02594 *
copper 0.1666 0.7501 0.222 0.8243
iron -1.209 0.8336 -1.45 0.147
manganese 0.09805 0.7507 0.1306 0.8961
mercury 0.2309 0.5034 0.4587 0.6464 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.2 1.33 2.21 1.14 1.65 1.95 2.55 2.3 2.64 1.84

Influence indices

## McFadden's pseudo-R2 is: 0.35 
## Tjur's pseudo-R2 is: 0.36 
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -95.62 9421 -0.01015 0.9919
aquaculture -5.991 3.543 -1.691 0.09081
city -20.51 13.22 -1.552 0.1207
dredging_collect 10.22 4.15 2.463 0.01376 *
dredging_dump 2.973 3.365 0.8835 0.3769
industry 3.481 1.621 2.148 0.03173 *
shipping_mooring 8.818 5.28 1.67 0.09491
shipping_traffic -0.3772 1.361 -0.2771 0.7817
sewers_rain 15.1 5.968 2.53 0.01141 *
sewers_waste -22.76 8.693 -2.619 0.008829 * *
wharves_city 14.73 10.75 1.37 0.1707
wharves_industry -16.4 6.847 -2.396 0.01659 *
fisheries_trap 0.3183 0.4545 0.7003 0.4838
fisheries_trawl -0.1167 0.3576 -0.3263 0.7442
fisheries_net -967.5 97683 -0.009905 0.9921
fisheries_dredge 2.436 1.528 1.594 0.1109 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 13.3 34.5 12.4 9.74 3.59 18.9 3.94 19.3 30.5 29.9 19.8 1.16 1.46 1 2.8

Thyasira sp

## SDM for: thyasira_sp

Abiotic parameters

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 42341 -0.0006274 0.9995
om 9.706e-15 75659 1.283e-19 1
gravel -2.813e-15 46245 -6.082e-20 1
silt -9.501e-15 81058 -1.172e-19 1
clay 6.063e-15 115994 5.227e-20 1
arsenic 5.656e-15 63425 8.917e-20 1
cadmium 2.434e-15 55192 4.411e-20 1
copper -1.264e-14 72634 -1.74e-19 1
iron 4.18e-15 51051 8.188e-20 1
manganese -7.198e-15 75825 -9.492e-20 1
mercury 8.982e-15 64044 1.402e-19 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.96 1.33 2.14 1.09 1.56 1.44 1.91 1.39 1.85 1.56

Influence indices

## McFadden's pseudo-R2 is: 0 
## Tjur's pseudo-R2 is: NaN 
## Pearson's pseudo-R2 is: NA
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -26.57 39784 -0.0006678 0.9995
aquaculture -4.492e-15 354336 -1.268e-20 1
city 2.363e-15 321138 7.357e-21 1
dredging_collect -2.952e-15 254116 -1.162e-20 1
dredging_dump 2.828e-15 294996 9.588e-21 1
industry 6.103e-15 160225 3.809e-20 1
shipping_mooring -2.342e-15 271832 -8.617e-21 1
shipping_traffic 8.126e-15 121076 6.712e-20 1
sewers_rain -4.695e-15 356417 -1.317e-20 1
sewers_waste 4.678e-15 479508 9.757e-21 1
wharves_city 8.617e-16 384550 2.241e-21 1
wharves_industry -1.402e-14 419585 -3.34e-20 1
fisheries_trap 6.28e-16 38619 1.626e-20 1
fisheries_trawl -3.839e-15 46815 -8.201e-20 1
fisheries_net 2.097e-17 38310 5.473e-22 1
fisheries_dredge -1.4e-15 103236 -1.356e-20 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 9.47 8.46 6.53 7.68 4.32 6.88 3.23 9.16 12.4 9.99 10.9 1.08 1.35 1.11 1.66

Trichotropis bicarinata

## SDM for: trichotropis_bicarinata

Abiotic parameters

## McFadden's pseudo-R2 is: 0.16 
## Tjur's pseudo-R2 is: 0.02 
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -107.6 38257 -0.002812 0.9978
om 0.2478 2.756 0.08991 0.9284
gravel -30.79 31433 -0.0009794 0.9992
silt 0.04291 2.451 0.0175 0.986
clay -510.4 203220 -0.002512 0.998
arsenic 0.2016 3.626 0.05559 0.9557
cadmium -0.2526 2.154 -0.1173 0.9066
copper 0.3548 2.141 0.1657 0.8684
iron -0.07093 1.132 -0.06269 0.95
manganese -1.669 5.303 -0.3148 0.7529
mercury -1.39 3.01 -0.4617 0.6443 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 1.8 1 1.87 1 1.38 1.67 1.6 1.51 1.82 1.46

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -76.04 139169 -0.0005464 0.9996
aquaculture -249 1551028 -0.0001605 0.9999
city -159.9 2117161 -7.554e-05 0.9999
dredging_collect -109.7 1596207 -6.872e-05 0.9999
dredging_dump -46.45 1678940 -2.767e-05 1
industry 133.7 498328 0.0002684 0.9998
shipping_mooring -190.8 2202337 -8.662e-05 0.9999
shipping_traffic -42.27 1309807 -3.228e-05 1
sewers_rain 66.56 2123791 3.134e-05 1
sewers_waste -71.06 3039533 -2.338e-05 1
wharves_city 274.8 3128180 8.784e-05 0.9999
wharves_industry 53.93 1331590 4.05e-05 1
fisheries_trap -49.95 180585 -0.0002766 0.9998
fisheries_trawl 3.445 1351368 2.55e-06 1
fisheries_net 4.872 117130 4.16e-05 1
fisheries_dredge -2.791 858084 -3.253e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 28.3 27.5 27.7 25.6 9.14 35.8 24.4 34.4 53.8 42.1 22.7 2.21 10.3 1.26 5.39

Turritellopsis stimpsoni

## SDM for: turritellopsis_stimpsoni

Abiotic parameters

## McFadden's pseudo-R2 is: 0.52 
## Tjur's pseudo-R2 is: 0.36 
## Pearson's pseudo-R2 is: 0.35
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -190.7 81645 -0.002336 0.9981
om 6.819 5.27 1.294 0.1957
gravel -1.005 1.007 -0.9974 0.3186
silt -6.932 4.992 -1.389 0.165
clay -990.2 445526 -0.002223 0.9982
arsenic 2.756 3.336 0.8262 0.4087
cadmium -2.536 2.245 -1.13 0.2586
copper 2.35 2.328 1.009 0.3128
iron -5.67 4.496 -1.261 0.2072
manganese -3.814 4.669 -0.8168 0.4141
mercury 1.607 2.028 0.7924 0.4281 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 5.51 1.77 5.27 1 2.24 2.73 2.84 3.76 2.66 2.41

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -980.4 2402282 -0.0004081 0.9997
aquaculture 568 3216366 0.0001766 0.9999
city 2235 8062568 0.0002772 0.9998
dredging_collect 818.8 4001270 0.0002046 0.9998
dredging_dump 1251 3427008 0.0003651 0.9997
industry -408.4 1738626 -0.0002349 0.9998
shipping_mooring -555.1 11064629 -5.017e-05 1
shipping_traffic 349.4 1883953 0.0001854 0.9999
sewers_rain 1472 4521220 0.0003256 0.9997
sewers_waste -1592 10779339 -0.0001477 0.9999
wharves_city -2943 10350412 -0.0002843 0.9998
wharves_industry -925.7 9048771 -0.0001023 0.9999
fisheries_trap -646.5 4467910 -0.0001447 0.9999
fisheries_trawl -79.34 690430 -0.0001149 0.9999
fisheries_net -13.16 6464067 -2.035e-06 1
fisheries_dredge 4.467 288264 1.55e-05 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 43.6 131 73.1 41.5 27.6 154 32.7 109 191 138 126 10.2 11.3 1 2.56

Yoldia myalis

## SDM for: yoldia_myalis

Abiotic parameters

## McFadden's pseudo-R2 is: 0.07 
## Tjur's pseudo-R2 is: 0.02 
## Pearson's pseudo-R2 is: 0.01
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -347.3 90666 -0.003831 0.9969
om 0.4722 1.254 0.3765 0.7065
gravel -0.02457 0.5421 -0.04533 0.9638
silt -0.6866 1.217 -0.5644 0.5725
clay -1877 494750 -0.003793 0.997
arsenic 0.1898 1.177 0.1612 0.8719
cadmium -0.216 0.9361 -0.2308 0.8175
copper -0.3942 1.33 -0.2963 0.767
iron -0.4075 1.106 -0.3686 0.7124
manganese 0.4142 1.367 0.303 0.7619
mercury 0.1164 1.028 0.1133 0.9098 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  om gravel silt clay arsenic cadmium copper iron manganese mercury
VIF 2.18 1.63 2.16 1 1.58 1.45 2.17 1.63 2.45 1.81

Influence indices

## McFadden's pseudo-R2 is: 1 
## Tjur's pseudo-R2 is: 1 
## Pearson's pseudo-R2 is: 1
Fitting generalized (binomial/logit) linear model: model
  Estimate Std. Error z value Pr(>|z|)
(Intercept) -542 1844508 -0.0002939 0.9998
aquaculture -252.7 13894638 -1.819e-05 1
city -381.4 19798867 -1.927e-05 1
dredging_collect -735.9 17800592 -4.134e-05 1
dredging_dump 48.62 14174691 3.43e-06 1
industry 381.4 4913263 7.762e-05 0.9999
shipping_mooring 501.4 18081853 2.773e-05 1
shipping_traffic 197.8 2449158 8.074e-05 0.9999
sewers_rain 582.1 4387610 0.0001327 0.9999
sewers_waste -1324 3699990 -0.0003579 0.9997
wharves_city 56.61 18776123 3.015e-06 1
wharves_industry 427 32498535 1.314e-05 1
fisheries_trap -84.43 3438724 -2.455e-05 1
fisheries_trawl -300.3 4677720 -6.421e-05 0.9999
fisheries_net 21.64 6461332 3.349e-06 1
fisheries_dredge -26.96 5328338 -5.061e-06 1 
## No RMSE calculation available for logistic models
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 258 252 193 162 35.2 293 30.5 54.8 56.8 263 354 17.5 7.98 1 45.2

2. Density data

To be added.

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